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DESIGN AND OPTIMIZATION OF MILK-RUN

MATERIAL SUPPLY SYSTEM WITH

SIMULTANEOUS PICKUPS AND DELIVERIES IN

TIME WINDOWS

A Thesis submitted to Gujarat Technological University

for the Award of

Doctor of Philosophy

in

Mechanical Engineering

By

Dipteshkumar Rameshbhai Patel

Enrollment No.129990919004

under supervision of

Dr. Mulchandbhai B. Patel

GUJARAT TECHNOLOGICAL UNIVERSITY

AHMEDABAD

[November-2017]

i

DESIGN AND OPTIMIZATION OF MILK-RUN

MATERIAL SUPPLY SYSTEM WITH

SIMULTANEOUS PICKUPS AND DELIVERIES IN

TIME WINDOWS

A Thesis submitted to Gujarat Technological University

for the Award of

Doctor of Philosophy

in

Mechanical Engineering

By

Dipteshkumar Rameshbhai Patel

Enrollment No.129990919004

under supervision of

Dr. Mulchandbhai B. Patel


AHMEDABAD

[November-2017]

ii

© Dipteshkumar Rameshbhai Patel

iii

DECLARATION

I declare that the thesis entitled “Design and Optimization of Milk-Run Material Supply

System with Simultaneous Pickups and Deliveries in Time Windows” submitted by me

for the degree of Doctor of Philosophy is the record of research work carried out by me

during the period from October-2012 to March- 2017 under the supervision of Dr.M.B.Patel

and his has not formed the basis for the award of any degree, diploma, associateship,

fellowship, titles in this or any other University or other institution of higher learning.

I further declare that the material obtained from other sources has been duly acknowledged

in the thesis. I shall be solely responsible for any plagiarism or other irregularities, if

noticed in the thesis.

Signature of the Research Scholar: Date:….………………

Name of Research Scholar: Dipteshkumar Rameshbhai Patel

Place: Ahmedabad

iv

CERTIFICATE

I certify that the work incorporated in the thesis title as Design and Optimization of Milk-

Run Material Supply System with Simultaneous Pickups and Deliveries in Time

Windows submitted by Mr. Dipteshkumar Rameshbhai Patel was carried out by the

candidate under my supervision/guidance. To the best of my knowledge: (i) the candidate

has not submitted the same research work to any other institution for any degree/diploma,

Associateship, Fellowship or other similar titles (ii) the thesis submitted is a record of

original research work done by the Research Scholar during the period of study under my

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Name of Supervisor: Dr. M.B.Patel

Place: Ahmedabad

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Supply System with Simultaneous Pickups and Deliveries in Time Windows” by Mr.

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Dipteshkumar Patel Thesis

ORIGINALITY REPORT

%17 %15 %12 %2 SIMILARITY INDEX INTERNET SOURCES PUBLICATIONS STUDENT PAPERS

PRIMARY SOURCES

www.ijsrd.com

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Internet Source

www.iieom.org

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2

Internet Source

Sule Itir Satoglu. "Design of a just-in-time %1

3

periodic material supply system for the

assembly lines and an application in electronics

industry", The International Journal of

Advanced Manufacturing Technology,

05/05/2012

Publication

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http://www.iaeng.org/

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Publication

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Submitted to Tashkent International School %1 8 Student Paper

Liang Chun-Hua. "Vehicle routing problem %1

9

with time windows and simultaneous pickups and deliveries", 2009 16th International Conference on Industrial Engineering and Engineering Management, 10/2009

Publication

www.ijaiem.org %1

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academicjournals.org %1

11 Internet Source

www.logistics.eng.chula.ac.th %1

12 Internet Source

ethesis.nitrkl.ac.in %1

13 Internet Source

14

Tasan, A.S. "A genetic algorithm based approach to vehicle routing problem with simultaneous pick-up and deliveries", Computers & Industrial Engineering, 201204

%1

http://www.ijaiem.org/

viii

PhD THESIS Non-Exclusive License to


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ix

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Signature of the Research Scholar: Date: ….………………


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THESIS APPROVAL FORM

The viva-voce of the PhD Thesis submitted by Shri Dipteshkumar Rameshbhai Patel

(Enrollment No.129990919004) entitled Design and Optimization of Milk-Run Material

Supply System with Simultaneous Pickups and Deliveries in Time Windows was

conducted on …………………….………… (day and date) at Gujarat Technological

University.

(Please tick any one of the following option)

The performance of the candidate was satisfactory. We recommend that

he/she be awarded the PhD degree.

Any further modifications in research work recommended by the panel after 3

months from the date of first viva-voce upon request of the Supervisor or request

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he/she should not be awarded the PhD degree.

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Name and Signature of Supervisor with Seal

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xi

ABSTRACT

Material handling is one of the most crucial issues that should be taken into account for

eliminating waste, reducing the cost and just in time-based delivery of the product. Many

industries are spending millions of bucks for the transportation of the goods. An effective

transportation management system has to be implemented to control the cost of

transportation and inventory. The optimized milk-run concept can be utilized to overcome

the issue related to the supply chain management system. The milk-run material supply

system is the cyclic trips, where either good are collected from several suppliers and

delivered to one customer, or goods are collected from one supplier and delivered to several

customers. The objective of this research is the minimization of the total material handling

and inventory holding cost. It is also concentrated on just in time delivery to enhance the

customer satisfaction. These saving of cost could be either used for reduction of the product

cost, which will boost up the sales or lift the profit margin of the organization.

The purpose of this research is to develop a mathematical model and a heuristic approach,

which is utilized to construct the routes, calculate the associated cost and determine the

service period for the design of a milk-run material supply system with simultaneous pick-

up and delivery. The material supply by this system occurs on a just-in-time basis from

regional offices to several stations of the courier service. Besides, the proposed heuristic

approach intends to construct routes based on an initial service period value and attempts

to improve the solution by considering different period values. Furthermore, the scheduling

of the vehicles is calculated based on demands and routes of the network. The most

optimum solution is decided on the basis of the least total transportation cost and minimum

time. A genetic algorithm is proposed to solve the vehicle routing with simultaneous pickup

and delivery within time frame (VRPTWSPD) problem related to the milk-run concept.

The algorithm is applied with the help of Matlab and results are presented. The result

showed the performance and effectiveness of the algorithm

xii

ACKNOWLEDGEMENT

I would like to take an opportunity to express my sincere appreciation, who have guided

and supported me during this journey.

Firstly, I am extremely grateful to my honorable supervisor Dr. M. B. Patel, for his

continuous guidance, motivation, encouragement and support for throughout my research

work. His guidance helped me in all the time of research and writing of this thesis, I could

not have imagined having a better advisor and mentor for my research work.

I would like to express my sincere thanks to Dr. Jeetendra Vadher for valuable guidance.

His deep insights supported me at various phases of my research. His valuable suggestion

and constructive criticisms from time to time enabled me to complete my work

successfully.

Besides my advisor, I would like to appreciate to my Doctoral Progress Committee Members

Dr. Jeetendra Vadher, Professor, Government Engineering College, Palanpur and Dr.

Mangal Bhatt, Professor, Shantilal Shah Engineering College, Bhavnagar for their rigorous

examinations and precious suggestion during my research.

I am thankful to Dr, Akshai Aggarwal, Ex. Vice Chancellor, Dr. Navin Sheth, Vice

Chancellor, Shri J. C. Lilani, Registrar and all staff members of Ph.D. Section, GTU.

I would also like to acknowledge the guidance provided by each and every member of

Shankersinh Vaghela Bapu Institute of Technology, Gandhinagar. Without their

support, it may not be possible to reach at this stage of my journey of research.

Finally, I would like to thanks my father Mr. Rameshbhai Patel and my mother Mrs.

Bhanumati Patel for supporting me spiritually during a hard time of this journey. I would

also like to thank my beloved wife Kinjal Patel and my son Aarav for their unconditional

love and moral support.

xiii

Table of Contents

CHAPTER - 1 Milk-run material supply system ……………………………………….1

1.1 Introduction ...…………………………………………………………………………..1

1.2 Milk-run concept ...……………………………………………………………………..2

1.3 History of milk-run material supply system ...…………………………………...….….3

1.4 Lean logistic ...………………..……………………………………………………. .…3

1.5 Major challenges of the material supply system ..………………………….…………..4

1.6 Heuristic search algorithm .……………………………………………….……..……...5

1.7 Keywords .…………………………………………………………………....…………8

CHAPTER - 2 Literature review .…………………………………………………..........9

2.1 Just in time material supply system ………………………….…………………………9

2.2 Vehicle routing in milk-run operations .……………………………………………....11

2.3 Classification and modelling for milk-run distributions system ………...…………....12

2.4 Vehicle routing and scheduling ……………………………………………………….13

2.5 VRP with time windows and simultaneous pick-up and delivery …………………….15

2.6 VRP with split loads …………………………………………………………………..17

2.7 Flexible milk-run for stochastic vehicle routing ………………………………………18

2.8 Milk-run vehicle routing approach for shop-floor logistics …………………………..19

2.9 Milk-run distribution system for the express industry ………………………………...19

2.10 Robust optimization approach for milk-run problem with time window ……….…...20

2.11 Heuristic algorithm for single and multiple depot in VRP …………………….….....21

2.12 VRP with simultaneous pick-up and delivery based on customer satisfaction ……...23

2.13 Milk-run approach by genetic algorithm …………………………………….………23

2.14 Simultaneous pick-up and delivery in VRP with hybrid and multi-objective GA…...25

2.15 Modelling issues and heuristic solution approaches in VRP …………………….......27

2.16 Hybrid meta-heuristic algorithm for VRPSPD problem ………………………..…...27

2.17 Simultaneous pick-up and delivery with decision support system …………….….....28

2.18 Milk-run problem with time windows under inventory uncertainty …………….......30

2.19 Effect of milk-run supply system in global warming ……………….…………..…...31

2.20 Research Gap ………………………………………………………………………...32

CHAPTER - 3 Necessity, Objective and scope of study..……………………………....34

3.1 Problem Statement …………………………………………………………………….34

3.2 Necessity ………………………………………………………………………………34

xiv

3.3 Objective of research work ……………………………………………………………35

3.4 Scope of study ………………………………………………….……………………..36

3.5 Methodology of research ……………………………………………………………...37

CHAPTER - 4 Research methodology ………………………………………………….39

4.1 Formulation of mathematical model ……………………………………….………….39

4.1.1 Assumptions …………………………………………………………….....………..39

4.1.2 Objective function …………………………………………………………..………39

4.1.3 Constraints …………………………………………………………………..………40

4.1.4 Notations ……………………………………………………………………..……...41

4.2 Heuristic approach ………………………………………………………………..…...42

4.2.1 Tabu search algorithm ………………………………………………………………43

4.2.2 Simulated annealing …………………………………………………………….......45

4.2.3 Prim algorithm ………………………………………………………………..…......46

4.2.4 Genetic algorithm ……………………………………………………………..….....49

4.3 Validation ………………………………………………………………………..……50

4.3.1 Existing material supply system ……………………………………………….……51

4.3.2 Milk-run material supply system ………………………………………………........51

CHAPTER - 5 Result and discussion …………………………………………………...68

5.1 Development of routes ………………………………………………………………...68

5.1.1 Mehsana zone …………………………………………………………………….....69

5.1.2 Ahmedabad zone ………………………………………………………………........71

5.1.3 Rajkot zone …………………………………………………………………….........74

5.1.4 Vadodara zone ………………………………………………………………………77

5.1.5 Surat zone ……………………………………………………………………...........79

5.1.6 Gujarat region ………………………………………………………………..……...81

5.2 Express service ……………………………………………………………….….........83

5.3 CO2 emission …………………………………………………….……………...…….85

5.4 Vehicle routing database ………………………………………………………...........87

5.5 Development of routes with uncertain demand ………………………………….........89

CHAPTER - 6 Conclusion and future scope ……………………………………….......93

6.1 Conclusion ………………………….…………………………………………………93

6.2 Future scope ……………………………………………………………………….......94

List of references ……………………..…………………………………………………..95

List of publication ………………………………………..……………………………..102

xv

List of Abbreviations

VRPTWSPD Vehicle Routing with Simultaneous Pickup and Delivery with Time

Window

VRP Vehicle Routing Problem

JIT Just In Time

GA Genetic Algorithm

JITVRP Just In Time Vehicle Routing Problem

JITVRPSPD Just In Time Vehicle Routing with Simultaneous Pickup and

Delivery

CVRP Capacitated Vehicle Routing Problem

VRPTW Vehicle Routing Problem with Time Window

VRPB Vehicle Routing Problem Backhauls

VRPPD Vehicle Routing Problem with Pick-Up and Deliveries

MDVRP Multiple Depots Vehicle Routing Problem

PVRP Periodic Vehicle Routing Problem

SDVRP Split Delivery Vehicle Routing Problem

VRPSPD Vehicle Routing Problem Simultaneous Pick-Up and Delivery

MCPSO Multi Swarm Cooperative Particle Swarm Optimization

VRPSDPSLTW Vehicle Routing Problem Simultaneous Deliveries and Pickups with

Split Loads and Time Windows

VRPSDPD Simultaneous Pickup and Delivery for Single Depot and Multiple

Depot

TSSA Tabu Search and Simulated Annealing

SA Simulated Annealing

DSS Decision Support System

GPS Global Positioning System

TSP Travelling Salesman Problem

xvi

List of Figures

FIGURE 1.1: Milk-run supply system for manufacturing unit ……..……….……….…..2

FIGURE 1.2: Flow chart of genetic algorithm …………………………...…….…….…..7

FIGURE 2.1: Milk-run supply procurement system ..……………………….…….…….11

FIGURE 2.2: Classification of milk-run distribution system ……..………….………...13

FIGURE 2.3: Vehicle routing example ..……………………………………...………...14

FIGURE 2.4: Classification of VRP …………………………………………..………...16

FIGURE 2.5: Construction of path …………………………………………...………...20

FIGURE 2.6: Vehicle routing in multiple depot ……………………………...………....22

FIGURE 2.7: Vehicle routing by multi-objective genetic algorithm …………………..26

FIGURE 2.8: Components of VRP in decision support system ……………..………....29

FIGURE 4.1: Flow chart of tabu search ……………………………..………..…………44

FIGURE 4.2: Flow chart of simulated annealing …………………………...…………...46

FIGURE 4.3: Flow chart of prim algorithm ………………………………...…………...48

FIGURE 4.4: Flow chart of genetic algorithm ………….………………...…..…………50

FIGURE 5.1: Map of Gujarat state ………………………………...…….……………...69

FIGURE 5.2: Scheduling and routing for route 1 for Mehsana zone ………….………..70

FIGURE 5.3: Scheduling and routing for route 2 for Mehsana zone…..………………..70

FIGURE 5.4: Scheduling and routing for Mehsana zone ………………….………..…..71

FIGURE 5.5: Scheduling and routing for route 1 for Ahmedabad zone………….……..72

FIGURE 5.6: Scheduling and routing for route 2 for Ahmedabad zone ………………..72

FIGURE 5.7: Scheduling and routing for route 3 for Ahmedabad zone ………………..73

FIGURE 5.8: Scheduling and routing for Ahmedabad zone ……………….…....…..….73

FIGURE 5.9: Scheduling and routing for route 1 for Rajkot zone ………….…………..74

FIGURE 5.10: Scheduling and routing for route 2 for Rajkot zone …………….………75

FIGURE 5.11: Scheduling and routing for route 3 for Rajkot zone …………….………75

FIGURE 5.12: Scheduling and routing for route 4 for Rajkot zone ………….………....76

FIGURE 5.13: Scheduling and routing for Rajkot zone ………………………...………76

FIGURE 5.14: Scheduling and routing for route 1 for Vadodara zone………….……....77

FIGURE 5.15: Scheduling and routing for route 2 for Vadodara zone ………...….........78

FIGURE 5.16: Scheduling and routing for Vadodara zone ……………...……….……..78

xvii

FIGURE 5.17: Scheduling and routing for route 1 for Surat zone ……………...………79

FIGURE 5.18: Scheduling and routing for route 2 for Surat zone …………...…………80

FIGURE 5.19: Scheduling and routing for Surat zone …………………..…….………..80

FIGURE 5.20: Scheduling and routing of Gujarat region ……………………….……...81

FIGURE 5.21: Express service from Unjha to Vapi …………………………….............84

FIGURE 5.22: Express service from Rapar to Godhara …………………………...........84

FIGURE 5.23: Express service from Junagadh to Gandhinagar …………………..........85

FIGURE 5.24: CO2 emission of Gujarat region ………………….………………..........86

FIGURE 5.25: Vehicle routing android application …………………………………….87

FIGURE 5.26: Vehicle routing weight analysis …………………………………...........88

FIGURE 5.27: Development of routes with uncertain demand (Case: 1) ………………90



xviii

List of Tables

TABLE 4.1: Comparison of Various Algorithms for Milk-Run Supply System ……..……43

TABLE 4.2: Existing Material Supply System ………………….………………………...51

TABLE 4.3: Milk-run Material Supply System ………………….……………………......51

TABLE 4.4 Comparison of Existing and Milk-Run Supply System…..…………………...67

TABLE 5.1: Comparison of Conventional Supply System and Milk-Run Material

Supply System (Distance and Overall Cost) …………….…………………...82

TABLE 5.2: Comparison of Conventional Supply System and Milk-Run Material

Supply System (Travelling Time) …………………………………………...83

xix

List of Appendices

Appendix 1: Graphical User Interface……………………………………………..…..…103

Appendix 2: Database of Destination City……………………………………………..…108

Appendix 3: Database of Starting City………………………………………………....…112

Appendix 4: Calculation of CO2 Gas Emission …………………………..…………...…116

Appendix 5: Coding of Milk-Run Routing Supply System ………………….……..……121

Appendix 6: Multiple Travelling Salesman Problem with Genetic Algorithm ….…......…125

Appendix 7: Milk-Run Route with Weight Distribution ………...………………..….…..136

Appendix 8: Scheduling Of Individual Routes…………………………………….…..…143

Appendix 9: Graphical Representation of Milk-Run Routing System…………….......….149

Appendix 10: Construction of Express Routing…………………………….……..……...158

Appendix 11: Development of Dynamic Milk-Run Material Supply System...………......162

1

CHAPTER – 1

Milk-Run Material Supply System

1.1 Introduction

Material handling is a field of growing interest for both industries and research institutions.

Enormous rivalry in today’s global markets, the range of products with shorter life cycles, and

the higher expectations of consumer markets have enforced suppliers to concentrate on their

supply chains services. This, together with innovation and invention in communications and

transportation technologies has encouraged the continuous evolution of the supply chain

techniques. Consequently, to shrink cost and enhance service facility, operational supply chain

policies need to be taken into account.

The supply chain, which is also referred to as the logistics network involves a bunch of varieties

of management (suppliers, manufacturing unit, warehouses, distribution point, and retail

outlets). The material supply system includes the movement of goods from a supplier to a

customer, as well as any customer who returns to the supplier in a logistic distribution network.

It involves supply, storage and control of materials and products throughout distribution in the

system. Material supply system plays an important role in manufacturing and logistic. The

vehicle routing problem is a common central problem in various industries, required to

optimally route a fleet of vehicles to several sets of consumers with simultaneous pickup and

delivery. The environment in which industries nowadays accomplish their supply chain is highly

vigorous. If an effective material supply system is established, it is claimed that the cost will be

reduced by 10% to 30% [2]. Moreover, in a typical manufacturing industry, material handling

accounts for 25% of workers, 55% of all factory space, and 87% of the production time [3].


2

1.2 Milk-Run Concept

A milk-run is an effective logistic system, which is round trip that facilitates either delivery or

pick-up of the goods. Milk-run supply system can be primarily classified into two categories

external milk-run system and internal milk-run system. Internal milk-run systems consist of

supply of parts from origin to the destination that takes place inside the plant. External milk-run

supply system involves product delivery from main warehouses (origin) to the customer

(destination). Application of milk-run distribution systems in plants is to standardize the

material handling system and eliminates the waste effectively.

Milk-run material supply system is the round trips, where either goods collected from multiple

suppliers and transported to single customer or goods collected from a single supplier and

transported to multiple customers within the predefined path (Fig. 1.1) in a cyclic manner. The

size and length of the routes are predefined, which are considered according to the volume of

the product and carrying capacity of the vehicle. The allocated vehicle has to travel through the

assigned routes at the time of collection or the delivery of the products.

FIGURE 1.1: Milk-Run Supply System for Manufacturing Unit

(Source: http://3rdpartylogistics.blogspot.in/2011/09/what-is-milk-run-system-transportation.html)

http://3rdpartylogistics.blogspot.in/2011/09/what-is-milk-run-system-transportation.html

History of Milk-Run Material Supply System

3

This research describes “milk-run” as a concept that is a sequential collection of the product

from multiple sources and the direct service to the delivery points within the time frame. The

prime objective of the milk-run material supply system is to minimize the total transportation

and inventory cost.

1.3 History of Milk-Run Material Supply System

The word “milk-run” was introduced in the USA by the milkman during the 19th century, who

used to supply the milk bottles at the door steps of each customer. On the daily basis, the

milkman simultaneously used to deliver the full bottles of milk and pick-up the empty bottles in

the cyclic trip. At the end of the trip, he used to return with the bunch of empty bottles, which

got filled on the next day and trip initiated with the starting point again. During the 20th century,

this technique was implemented in industrial supply chain management system by the

organisation. Soon it became significant concept among many industries due to better control

of inventory and total production cost. Later on, milk-run concept was implemented in aircraft

industries with a long distance flight with multiple stops, military patrol route surveillance,

public transport, courier services and many logistic industries. Nowadays it has become the

significant tool in logistic services of various field of the organisation. Even within the area of

milk-run material supply system, a number of developments have occurred over the past years.

1.4 Lean Logistic

Lean logistics is always driving innovation in the supply chain management system and

enormous benefits are drawn by innovative technology for organisations and consumers. The

objectives of the lean logistics are the delivery of the right mix of products at the right time to

the right place, and carry out these activities, effectively [1]. Assembly lines must satisfy the

customer’s demand neither late nor early, because early production incurs inventory holding

cost and late production causes either loss of sales or backlog. Therefore, part’s supply to the

point of use on the assembly line must be also achieved just-in-time (JIT). Otherwise, either

time losses may occur due to disorganized and insufficient material supply to the assembly line

or excess inventory accumulates that further increases loss to the industries. The exact number


4

of parts must be delivered to prevent inventory holding. Product delivery within just-in-time

enhances the reliability and satisfaction among the customers.

1.5 Major Challenges of the Material Supply System

The business environment has become increasingly competitive. Industries keep looking to find

a better way to manage and reduce the cost associated with the product. Supply chain

management plays a vital role as logistics costs have a major involvement in total production

cost. Many industries have implemented the just-in-time system to reduce total production costs.

On the other hand, the customer expectations have gone upwards to the sky due to the supersonic

era, while the capability of rivals to deliver superior quality within the short period of time and

at low cost has changed the phase of supply chain management. Uncertainty in demand affects

industries to manage the travel time of the vehicle, reliability among the customer, total transport

cost and inventory holding cost, which will lead dispersed industries to take the prices down

with no or less profit to sustain in the global competition or to remain competitive.

Today, assembly lines usually require many types of components at different stages of assembly

operations simultaneously. The need to move small quantities of a large number of items within

the plant with short and predictable lead times without increasing transportation costs resulted

in the development of milk-run material delivery systems [1]. The milk-run delivery system

picks up and delivers containers of parts along the fixed routes, each comprised of a

predetermined set of stock points of the stations of an assembly line, based on a schedule.

Therefore, integration of pull production control with the milk-run part supply system for the

assembly lines is beneficial. The way of creating an economical advantage is to maintain a

highly sustainable material supply system. Thus, logistics becomes an integral measure of the

product that is being delivered to the customer.

The trend of migrating to the urban area is increasing day by day for better education and

economic conditions, including availability of employment opportunity and higher pay rate in

major cities. At the forefront of the urbanization trend, traffic congestion issue is often reported

in the urban area. As the density of traffic increases on highways and urban roadways, supplier

and delivery service operators have a major challenge to cope with predefined schedules. The

Heuristic Search Algorithm

5

costs incurred as an effect of traffic jams are higher in the urban and its associated area. It has

been experienced by the shippers and carriers, that there are the day-to-day cost implications of

delay and the reliability increases which affects supply chain management adversely. It has now

become inevitable to adopt the innovative technique to deal with a major challenge of the globe.

The milk-run material supply system is a powerful tool to optimise the utilisation of vehicles in

supply chain network.

1.6 Heuristic Search Algorithm

The word heuristic is derived from ancient Greek region known to discover or to find something

innovative, is an approach to solve a practical cumbersome problem, solving by employing a

practical method associated with real life issue. It is used for algorithms which find best

solutions between all imaginable ones. The best example of heuristic search method is the

travelling salesman problem. Heuristic doesn’t ensure that the optimal solution will be found,

but these will be usually close to the optimal. Although the solution finding process is fast,

reliable and easy, most of the time this solution can be accurate that is actually near to the

optimum value. Therefore, a heuristic approach is beneficial to get close to the optimal solution,

within small computational time.

A genetic algorithm was introduced in 1970 by John Holland, in early to mid-1980s, genetic

algorithms were being applied to a broad range of subjects. A genetic algorithm is a heuristic

search algorithm based on mechanics of natural selection and genetics. This heuristic approach

is habitually used to produce suitable results for the optimization of the problem. It is an

evaluation theory which makes the selection natural to produce powerful next coming

generations. The genetic algorithm follows the principle of survival of the fittest laid down by

Charles Darwin. The basic components of the genetic algorithms are chromosomes, population,

fitness function, genetic operator (reproduction, crossover, mutation) and generations. The

random population contains a number of different chromosomes which have different objective

function results. A chromosome consists of genes which represent candidate solution or

features. The algorithm constantly modifies population of an individual solution by a fitness

function. At each phase, the genetic algorithm randomly selects individual from the recent group


6

of populations, called parents and generate the children as a future generation. This process is

repeated until the population convert into an optimal solution.

The flow chart (Fig. 1.2) describes the process of a genetic algorithm. The algorithm begins by

creating a random initial population. The algorithm then creates a sequence of new populations.

At each step, the algorithm uses the individuals in the current generation to create the child of

next generation population. To create the new population, the algorithm performs the following

steps:

It scores each member of the current population by computing its fitness value.

Parents are selected based on their fitness.

Some of the individuals in the current population have lower fitness value, which are

chosen as elite. These elite individuals are passed to the next population.

Children are produced from the parents of current generation. These children are formed

either by making random changes to a single parent (mutation) or by combining the

vector entries of a pair of parents (crossover).

New produced generation replaces the current population with the children to form the

next generation.

Heuristic Search Algorithm

7

FIGURE 1.2: Flow Chart of Genetic Algorithm

This process runs until the stopping criteria is met, that is the optimum solution of the problem.

There is a wide range of application of the genetic algorithm. It has been widely accepted in

many industries in various areas, some of them are described as below,

Optimization

Automatic Programming

Machine Learning

Economics

Gen = 0

Create initial random population

Designate best chromosome

found so far as a solution

Yes

Evaluate fitness of each individual in population

Reproduction

Crossover

Mutation

Gen = Gen + 1

If Gen=N

End

No


8

Immune systems

Ecology

Evolution and learning

Social systems

Scheduling

Layout and circuit design

Management Applications

Control

VLSI Design

Identification & Pattern recognition, etc.

1.7 Keywords

Supply Chain Management, Logistics, Vehicle routing problem, Just-in-time, Milk-run problem

with time window, Material supply system, Simultaneous pick-up and delivery, Lean logistic,

Optimization, Heuristic approach, Genetic algorithm, Mathematical model, Linear

programming, Inventory holding cost, Transportation cost, Penalty cost, Fixed cost.

9

CHAPTER – 2

Literature Review

An extensive literature survey was carried out on the theme of the milk-run material supply

system and optimisation of the vehicle routing problem with simultaneous pick-up and delivery

considering time window. During the review of milk-run supply chain management, papers of

two inter-related topics were studied: internal and external milk-run material supply system. The

various optimisations mathematical methods (algorithm) were reviewed and analysed to

understand the effect and limitations of computer modelling in solving vehicle routing problem.

The study literature review has directed to the necessity of the research in the field of supply

chain management for effective optimisation as transportation cost has a significant impact on

overall cost of the product or service.

2.1 Just-in-time Material Supply System

The just-in-time material supply system is the logistics measurement of the lean manufacturing

[1]. It represents lean philosophy to logistic activities supported within the plant among the

suppliers and the manufacturer. Vaidyanathan et al. [4] analysed the JITVRP (Just-in-time

vehicle routing problem) and emphasized the unique characteristic of this problem. JITVRP

requires that the quantity to be delivered at each of the demand nodes is a function of the route

taken by the vehicle assigned to serve that node. The authors developed a nonlinear

mathematical model for vehicle routing problem, relaxed it by making some assumptions and

proposed a linear model that attempts to determine the lower bound for the number of vehicles

required [4]. Moreover, a two-stage heuristic algorithm was proposed in research work, for the

optimal solution of the problem. Satoglu S. and Sahin E. [15] explained JIT periodic material

supply system for an assembly line. They approached heuristic algorithm and mathematical

method for routing. However, the researchers did not attempt scheduling of the vehicles in their

research.

Literature Review

10

The objective of the proposed mathematical model is the minimization of the total transportation

and inventory cost [15]. The proposed heuristic approach is anticipated for the construction of

routes based on an initial service period value and attempts to improve the optimal solution by

considering different period values.

Domingo et al. [9] explained an implementation of the milk-run material supply system that

serves a lean assembly line. Researchers have followed a practical approach. Initially, the stock

points where pickups and deliveries were determined. Then, the sequence of operations was

defined and alternative feasible routes were identified. The stock points with high demand rates

and those with lower demand rates were included in two separate routings. Then, the pickup and

delivery schedule were determined. However, the researchers did not attempt to make an

optimal milk-run delivery system design. Alvarez et al. [10] presented a case study of the

redesign of an assembly line by using lean production tools. To reduce lead time and excessive

stocks and to improve material flow within the manufacturing system, Kanban-based production

control and a milk-run material handling system were implemented. The authors realized that

the design of Kanban production control system is insufficient without implementing an

appropriate material handling vehicle [10]. Boysen and Bock [11] considered scheduling JIT

part supply to a mixed-model assembly line where assembly line and the warehouse are at

different factory floors. The authors used dynamic programming and simulated annealing for

the solution of the problem. Hao and Shen [8] developed a prototype software system that

integrates discrete event simulation with agent-based simulation technique to evaluate the

performance of a Kanban-based milk-run system serving an assembly line. In addition, Nemotoa

et al. [16] explained JIT external milk-run applications of the Toyota automobile assembly

factories located in Thailand. It has been revealed by the case study that implementing the milk-

run supply system even under highly congested traffic conditions can have full control on the

procurement process, which reduces the number of vehicles in supply network by optimisation.

It resolves the traffic problem to some extent level in urban areas and highways.

Vehicle Routing in Milk-Run Operations

11

2.2 Vehicle Routing in Milk-Run Operations

The vehicle routing involves optimisation of the routes for a fleet of vehicle to several sets of

consumers and supplier. In large scale industries, various transportation systems are considered

in order to deliver the material. However, it is very complicated especially dealing with large

scale industries with an enormous number of materials need to be delivered at various locations

[20]. Theeratham Meethet and Manoj Lohatepanont [21] have addressed the vehicle routing

problem in a milk-run system by applying optimization techniques to find the overall material

handling cost minimizing milk-run plan and satisfying the demands.

FIGURE 2.1: Milk-run Supply Procurement System [21]

The milk-run operation usually consists of a sequential collection of the goods from the various

supplier and delivery to the central warehouse. The milk-run material supply system can

increase vehicle utilization efficiency by doing the round trip to collect only required amount of

parts from each supplier shown in fig. 2.1. The authors have proposed a mathematical model

expressing two solution approaches; an ordinary mathematical programming algorithm and a

column generation based branch and bound algorithm. The model was tested with large scale

real world problems. The results described that the column generation based branch and bound

algorithm can solve the problems relatively quickly but at the expense of more complicated

Literature Review

12

implementation [21]. Authors also described that solving a problem by column generation based

branch and bound algorithm requires three efficient components: (i) linear programming solver,

(ii) branch and bound framework, and (iii) column generation implementation, whereas

mathematical modelling solve the problem with fewer resources.

Large scale real life manufacturing problem requires a different logistic approach to cope with

the demand of various goods. Effective vehicle routing approach needs to be adopted to bolster

the industries against the vehicle capacity and time consumption issues. Milk run schedule

approach is deliberated as a distinct vehicle routing problem (VRP) with time windows and a

restricted number of fleets. David Gyulai et al. [21] have proposed local search algorithm for

vehicle routing. Innovative approach has introduced that employ a primary solution generation

algorithm and a local search method to solve the milk run problem. The software prototype has

been verified with real-life industrial data to determine the abilities of the proposed solution.

The aim of authors is to test the model in real life condition to minimize the requirement of

vehicles in the supply chain network by reducing the time of the trip.

2.3 Classification and Modelling for Milk-Run Distributions System

Milk-run material supply system can be classified in various ways according to the application

in industry. Huseyin Selcuk Kilic et al. [37] have used assignment methodology for the

classification and modelling of the problem. The milk-run distribution system can be divided

into three main categories; such as general assignment problem, dedicated assignment problem

and determined time periods assignment problem as shown in fig. 2.2. Each main category can

be subdivided based on a number of routes and service period of the vehicle. The first category

general assignment problem can be sub divided as one routed vehicle and multiple routed

vehicles as routes and time period is unknown. These can be further divided as differently time

period and equally time period according to the time period. Dedicated assignment problem can

also be divided as differently time period and equally time period since the routes are known

but time period needs to be determined. The last category is sub divided into the one routed

vehicle and multiple routed vehicles since time periods are known but routes are unknown.

Vehicle Routing and Scheduling

13

FIGURE 2.2: Classification of Milk-run Distribution System [37]

Authors have proposed mixed integer linear programming model for the category called

“determined time period assignment problem” with multiple routed vehicle where the service

period time was known but a number of vehicles and routes are unknown. The results are

analyzed by constructing the numerical example. The result is compared to check the

performance; such as time and cost. However, it is seen that optimum result is difficult to

achieve in a large problem. More efficient heuristic approach needs to be developed for such

kind of problem.

2.4 Vehicle Routing and Scheduling

The scheduling of customer service and the routing of vehicles play a vital role in the supply

chain management system. Both the routing and scheduling of service vehicles have a major

impact on the quality of the supply chain industries. The objective of most routing and

scheduling problems is to minimize the total transportation cost of service within the predefined

Milk-run distribution

system

General

assignment

problem

Dedicated

assignment

problem

Determined

time period

assignment

problem

One

routed

vehicle

Multiple

routed

vehicle

Differently

time

period

Equally

time

period

Equally

time

period

Differently

time

period

Equally

time

period

Differently

time

period

One

routed

vehicle

Multiple

routed

vehicle

Literature Review

14

time. This includes vehicle capital costs, running cost, and fixed costs. The classification of

routing and scheduling problems depends on the size of the supply network, capacities of the

vehicles, and routing and scheduling objectives of the delivery system. It is classified as

travelling salesman problem, multiple travelling salesman, vehicle routing problem, Chinese

postman problem.

FIGURE 2.3: Vehicle Routing Example [36]

Routing and scheduling problems are often represented by graphical networks. Fig. 2.3 shows

the example of vehicle routing problem. It consists of five circles called station. Four stations

(2, 3, 4, and 5) represent pickup and delivery points, and remaining one station (1) represents a

depot. Tour of the vehicle initiated from the depot, which passes through each station and returns

to the origin at the end of the trip. Authors have discussed the features of vehicles routing,

vehicles scheduling, combined vehicles routing and scheduling problems. Various mathematical

programming methods are described to obtain an optimal solution for such types of problems.

Vehicle routing and scheduling of vehicles are an important factor to be handled by supply

chain service provider. An effective planning requires ensuring the necessity of the consumers

in terms of time and cost effectiveness. However, the objectives may vary according to the

nature of business; such as reducing customer inconvenience and reducing the time to serve the

VRP with Time Windows and Simultaneous Pick-Up and Delivery

15

customer. A solution of routing and scheduling problems initiates with an explanation of the

features of the service; such as whether activity on the nodes or the arrows, delivery-time

constraints, and capacity of vehicles. Several heuristic solution approaches can be employed to

obtain an effective solution. Authors have presented the nearest neighbor procedure and the

Clark and Wright savings heuristic for solving the vehicle routing and scheduling problem [36].

2.5 VRP with Time Windows and Simultaneous Pick-Up and Delivery

Vehicle routing problem simultaneous pick-up and delivery is a complex version of the classical

vehicle routing problem (VRP). This service often arise in supply chain network, for example

in the delivery of the product with the exchange offer, where new product needs to be delivered

to the consumer’s door step and against the new one, old or used product has to be picked-up

from the consumer within the certified time period. Another example is delivering perishable

goods to the retailer and collection of the empty container from the retailer for the packing

purpose within the predefined time period as there will be chances of loss if the product gets

melt. There are many types of VRP models which are employed according to the nature of

problem, such as Capacitated VRP (CVRP), VRP with time window (VRPTW), VRP with

backhauls (VRPB), VRP with pick-up and deliveries (VRPPD), Multiple depots VRP

(MDVRP), Periodic VRP (Deliveries in some days) (PVRP), Split delivery VRP (SDVRP);

which are shown in fig. 2.4. Some of the studies have considered time windows and VRPSPD

(vehicle routing problem simultaneous pick-up and delivery). Angelelli and Mansini [13]

formulated a mixed-integer nonlinear model of the VRP with time windows and simultaneous

pickup and delivery. The term of “time windows” means that the material handling vehicle's

pickup and delivery service at a supplier must be initiated and finished between the predefined

time periods.

Literature Review

16

FIGURE 2.4: Classification of VRP [19]

Vehicle routing problem with time window simultaneous pick-up and delivery (VRPTWSPD)

system involve both; delivery of a product from warehouse to the consumer and pick-up and

return goods from the consumer to the warehouse and each task time window, penalty cost may

implant due to the slow movement. Xiaobing Gan et al. [25] have worked on the uncertainty of

the number of vehicle and simultaneous delivery and pick-up service. The mathematical model

is constructed with the objective of optimum transportation cost. Multi swarm cooperative

particle swarm optimization (MCPSO) algorithm is applied and two dimensions encoding

method is proposed for VRPTWSPD. The result indicates that MCPSO algorithm is more

effective, simple and robust. It can be more significant for transport industries in the real world.

The proposed encoding method is beneficial to uncover and deal with the hidden problems

associated with the material supply system.

VRP with Split Loads

17

2.6 VRP with Split Loads

The concern of split load in vehicle routing problem is taken into account when the load

allocated to a particular vehicle is having irregular size than the capacity of a single vehicle. The

use of split loads method could minimize the total transportation cost at an extent level. Several

studies have been carried out on split load vehicle routing problem. Various heuristic methods

have been applied to resolve the problem. Ohlman, Fry and Thomas [14] considered the VRP

with time windows and split deliveries, where demands are picked up from a network of

suppliers and goods are delivered to the depot of a lean production system. The authors divided

the problem into two phases: the routing phase and the scheduling phase that is solved by a

heuristic approach. Similarly, Chuah and Yingling [5] developed a nonlinear mathematical

model of the VRP with time windows and considered high-frequency and small-quantity

deliveries from suppliers to a JIT assembly plant. Yong Wang et al. [23] formulated mixed

integer model for vehicle routing problem with simultaneous deliveries and pickups with split

loads and time windows. A hybrid heuristic algorithm is developed to solve the problem of

VRPSDPSLTW. Solomon datasets are applied with minor modifications to test the

effectiveness of the solution algorithm. The proposed algorithm established optimal solution

approaches for VRPSDPSLTW in terms of the total transportation cost, utilisation of vehicles

and loading rate. It might work as a universal analytical tool for optimizing vehicle routing in a

supply chain management system.

Chuanzhong Yin et al [24] studied the split load vehicle routing problem in order to optimise

the transportation cost by implementing simultaneous delivery and pickup with multi-element

vehicle routing problem. Authors have made certain preconditions and assumptions and

proposed tabu search algorithm to obtain the effective result. In this algorithm, nearest

neighbourhood method is used to achieve a basic feasible initial solution. Data were collected

from China Railway Express to test the effectiveness and feasibility of the model and algorithm

by comparing characteristics of solution convergence, solution quality and computing time with

those of present analogous studies. Results revealed that algorithm solved the problem

efficiently and achieve an optimum solution. VRP problem with split loads is applied widely in

the various area of a logistic network, especially when total delivery demands are larger than

Literature Review

18

the total loads [24]. However, it has been also noticed that the solution difference is more than

4% and it still needs to improve to achieve an optimal solution.

2.7 Flexible Milk-Run for Stochastic Vehicle Routing

Globalisation has raised the necessity to change the conventional vehicle routing problem.

Stochastic vehicle routing is a part of innovation in classical vehicle routing, in which demands

are probabilistic in nature rather than deterministic. The objective of the model is to minimize

the number of vehicle and travel time, which will lead to lower transportation cost. Supplier

faces the optimisation issue while handling with stochastic vehicle routing problem in which the

demands are split and uncertain. Various industries have employed this variant due to the

enormous advantages of the system. However, when a vehicle is dealing with multiple suppliers,

there is a chance of fluctuation in vehicle load, which may lead to underload or overload problem

in transportation. Flexible milk-runs design allows handling the variation of load in associated

route. The objective of consolidated shipment is to maintain great flexibility with respect to

variation in the volume of goods.

Ben Peterson et al. [38] have studied the vehicle routing problem with an objective to optimize

the sets of routes, such that the capacity of each vehicle is not exceeded with a given probability.

Authors have introduced a flexible milk-run model to optimize the stochastic demand problem.

The proposed method was designed to construct routes for North-American division of

Bosch/Siemens home appliance corporation. The solution consists of the careful selection of

supplier into consolidated routes, which is also called as flexible run. The model is designed in

such a way that change in volume of one supplier is inversely proportional to the change in

volume of another supplier. In other word, the vehicle capacity in each flexible route is higher

than demand. The concept is to cluster the stations in such a way that joint demand of all stations

is lower than the capacity of a vehicle. Thus the formed routes are flexible to the fluctuation of

demand. The result indicated a probability of the reduction of 25% of total transportation cost

along with the improvement of the robustness with respect to demand fluctuations.

Milk-run vehicle routing approach for shop-floor logistics

19

2.8 Milk-run vehicle routing approach for shop-floor logistics

The effective material supply system in shop floor logistics, especially in large scale production

unit is crucial to obtain. It has an important role in balancing the workload and ensuring a

continuous and smooth running of production. The shop-floor logistic system influences on the

performance of production system and order management. In order to manage material supply

system properly, efficient transportation system is required with an objective of minimum cost.

Various material supply systems are employed in order to manage the real world practical, large

scale shop floors. The effective vehicle routing approaches are required against the constraints

of a restricted capacity of vehicles and time to support production planning. David Gyulaia et

al. [22] have given an overview of the most effective solver algorithms of the vehicle routing

problem. Authors have proposed a unique approach that generates an initial basic solution and

applied local search method to solve the milk-run vehicle routing problem. The milk-run planner

employed that includes user interface and the solver algorithms in a .NET environment using

C# language. The proposed application offers user-friendly and efficient milk-run planning

environment. The proposed approach mainly focuses on the industrial necessities such as time

frame, effective layout planning and demand. In order to evaluate the effectiveness of the

solution, a software prototype has been developed and tested on real-life industrial data.

2.9 Milk-Run Distribution Systems for the Express Industry

Milk-run material supply system has been employed by the various logistic industries. However

very few have applied the milk-run system in express industries. As the world has become the

global village, competition of express delivery industry is passing through the bottle neck. Each

one is trying to cut down the transportation cost as low as possible along with the production

cost. It is a technique to minimize the transportation cost and improve quality of service in terms

of customer satisfaction. The combination between Milk-run material supply system and

express industry can stimulate the development of express enterprises.

Literature Review

20

Before Optimization After Optimization

FIGURE 2.5: Construction of Path [43]

Zhenlai You and Yang Jiao [43] have introduced milk-run supply system into the express

distribution industry based on a real scenario of the market through the feasibility analysis. This

module highlights the characteristics of the milk-run distribution system and described the size

and volume of routes and demand respectively. The proposed method improved the traditional

method and adopted multi-objective path optimization decision in order to satisfy full load

distribution; shown in fig. 2.5. It combines advantages of milk-run scheme and express

distribution that saves transportation cost by optimizing distribution scheme. The applied

traditional models used the savings algorithm to obtain the scheduling of the vehicles.The

module of proposed research is different from traditional distribution module. It has been seen

from the result analysis that feasible solution is achieved which effectively determined the route,

improved load factor and minimize cost.

2.10 Robust Optimisation Approach for Milk-Run Problem with Time

Window

The robust optimization approach is introduced to solve the milk run problem with time window

while uncertainty in inventory arises due to fluctuation in demand. The robust approach could

be implemented in action for the construction of routes that minimize transportation costs while

nourishing all inventories in a given restricted uncertainty due to volatility in the market. Jafari-

Eskandari M. et al. [19] have developed a mathematical formulation for the problem of robust

Heuristic Algorithm for Single and Multiple Depot in VRP

21

resource milk run, as a mixed integer approach to manage supply chain problems. The robust

optimization method was employed, mixed integer model was formulated for milk-run systems

with a time window and the results were compared with the optimal solutions of the proposed

milk run method. Uncertainty in supply in mathematical modelling has been considered as a

crucial issue in optimization [19]. There are two primary methods that have been proposed to

address uncertainty over the past years: (a) stochastic programming and (b) robust optimization.

Authors have proposed a deterministic, numerically tractable methodology to address a problem

of optimal control of supply chain subject to uncertain inventory. Robust optimization is

proposed in the research to obtain effective routing solutions for problems under uncertain

demand. It has been observed that robust optimization is an efficient alternative for formulating

milk-run problem under uncertainty since it does not require distribution assumptions on the

uncertainty or a cumbersome representation through scenarios [19]. The computational results

expressed that the robust solution can be protected from unmet inventory while incurring a little

additional cost over deterministic optimal routes. Results of investigation show that the robust

solution enforces additional cost to the transportation and inventory warehouse, though the

unnecessary inventory in the network is removed that make it more efficient than the

deterministic solution. Although the method is not implemented in large scale problems, it is a

suitable tool to validate the heuristic algorithms.

2.11 Heuristic Algorithm for Single and Multiple Depot in VRP

The vehicle routing problem with pickups and deliveries (VRPPD) is an extended version of the

classical vehicle routing problem (VRP), where customers may both receive and deliver goods

[52]. Single depot and multiple depot problems consist of the simultaneous delivery and pick-

up of goods from either one or multiple sources which makes it more complicated to solve and

achieve an optimal solution. In real life situation, due to the fact that capacity of demand is less

than the capacity of the vehicle, collection or delivery of the goods from the various stations has

become inevitable to reduce the transportation cost and improve the vehicle utilisation.

Literature Review

22

FIGURE 2.6: Vehicle Routing in Multiple Depot [32]

Nagy G. and Salhi S. [52] proposed heuristic approach of simultaneous pickup and delivery for

single depot and multiple depots (VRPSDPD).Fig. 2.6 represents the vehicle routing in multiple

depot. It has been assumed that the pickups and deliveries have taken place simultaneously or

according to the nature of the supply network. The method is proposed that treats pickups and

deliveries in an integrated manner with ignoring the theory of insertion. The approach tends to

find an optimal solution to the corresponding VRP problem and revises the result to make it

more feasible for the VRPSDPD. Such improvement is achieved mainly by heuristic approach

for routing of the vehicle adopted from VRP methodology but reformed such that the objective

becomes the declination of infeasibilities, although a number of problems for particular routing

is also constructed. The mathematical model has proposed to cope up the changes in a variation

of maximum demand. The proposed methodology is also proficient of solving single-depot and

multi-depot problems. The result achieved by the authors for single and multiple depot problems

are motivating for the industries and research institutes.

VRP with Simultaneous Pick-Up and Delivery based on Customer Satisfaction

23

2.12 VRP with Simultaneous Pick-Up and Delivery based on Customer

Satisfaction

In order to sustain in this global competitive market, higher customer satisfaction level is highly

desirable attribute. Product delivery should be taken place at right place at right time to enhance

the reliability among the consumers. Transportation service industries strive to improve the

customer satisfaction level as it is a key element to keep the reputation of the company. It is

clear that transportation industries should take effective operational decisions to optimize the

methods in supply chain more efficiently. Many industries have achieved the success at a certain

level based on considering customer satisfaction criteria as it reflects the quality of service. On

the other hand, time window has an inevitable impact in the operational decision since service

quality assessed by the customers is affected by the time required to serve the customer.

Although the number of vehicles is limited and all customer’s requirement cannot be achieved.

The vehicle routing problem with simultaneous pickup and delivery, considering customer

satisfaction is based on a time window at each customer location [26]. Transportation service

has to be as quick as possible to cope with such kind of problems, otherwise the penalty cost is

imposed to the overall cost. Jing Fan [26] has modelled VRPTWSPD to solve the minimization

of the total length of vehicle’s paths and maximization of all customer satisfaction to enhance

service quality. The author has considered the service quality is inversely proportional to the

waiting time for the delivery of product to the consumer. Customer satisfaction level improves

as the waiting time is reduced. The initial basic feasible solution is obtained by the nearest

neighbour method, which is improved by tabu search algorithm in later stages. Various group

tests have been undertaken and computational results indicate that problem size and time

windows have a different impact on the problem.

2.13 Milk-Run Approach by Genetic Algorithm

Genetic algorithm (GA) is a search heuristic based on the natural selection of evaluation theory.

It is often used to generate a solution of optimisation. A genetic algorithm is a random search

algorithm and widely used to solve the various kind of VRP problem. Liang Chun-Hua et al.

[27] proposed a mathematical model for VRPTWSPD and demonstrated the significance of the

Literature Review

24

introduction of time windows. The improved genetic algorithm is proposed for the problem.

Authors have used roulette wheel selection operator with an elitist model. They have also

adopted enhanced edge recombination crossover to improve the legacy of good characters of

the parents. GA has adopted an improved crossover to maintain the legacy of an excellent

chromosome and enhance capability of population search. Premature convergence is avoided

by applying a self-adaptation strategy to control crossover and mutation probability. Numerical

experiments have been conducted on a large scale test problem. Computational results were

admirable and proposed procedure represents an outstanding performance. However, there are

many uncertainties existing in a real situation, like the reliability of delivery and fluctuation in

demand.

Serdar Tasan and Mitsuo Gen [28] have studied an alternative approach to solve vehicle routing

problem with simultaneous pick-up and delivery (VRPSPD). The study has proposed a genetic

algorithm (GA) based approach, which uses permutation based representation and ensures

feasibility, for solving VRPSPD more efficiently and effectively. The computational model is

presented with parameter settings in order to illustrate the proposed approach. The performance

of the proposed approach is evaluated by solving several test problems formulated as mixed

integer linear programming models. Results of the solver are compared with the solution

provided by the developed approach and found to be efficient. Abbas Gharib Garakani and

Mohammad Reza Razzazi [29] have modeled genetic algorithm for multi capacitated depot

vehicle routing problem with pickup and delivery and time window, where the routes are

constructed in such a way that vehicles collect the demand and products are delivered to all

consumer with satisfactory level. The capacity of each depot is considered, which is different

from each other. Minimization of travelling costs, the fixed cost of depots and vehicles and

penalty cost of time window are the objective function of proposed research. During the

scheduling of vehicle, various constraints, such as time window, the capacity of depots and

capacity of vehicles are taken into account. A Genetic algorithm is used and solved by Matlab.

A set of feasible solutions to minimize total distance is generated and presented, which leads to

an optimal result.

Sadjadi SJ et al. [30] proposed a milk-run method as a mixed integer mathematical model and

meta-heuristic approach for a special case of auto-industry supply chain since problem belongs

Simultaneous Pick-Up and Delivery in VRP with Hybrid and Multi-Objective GA

25

to NP-Hard. Authors have developed a genetic algorithm to compare the result with an optimal

solution of proposed milk-run for large industrial case problems. The result indicated that the

proposed solution could be effective to minimize the cost of the logistic network. The primary

objective of the proposed model is to determine the pick-up time, diversify the order among the

suppliers and assignment of the routes to each vehicle.

2.14 Simultaneous Pick-Up and Delivery in VRP with Hybrid and Multi-

Objective GA

Due to the globalisation, business environment of supply chain industries has become more

challenging. It deals with managing product delivery to the customers with consistent quality at

reasonable low cost. Effective systems have become a basic need to achieve effectiveness and

efficiency. As it contributes to delivery processes in order to avoid tardiness and thus

minimizing transportation cost and improve the customer service level. Various delivery

methods are used by industries according to the nature of the problem. Milk-run material supply

method is used when the demand is less than full vehicle load per consumer. In milk-run, a

common vehicle delivers or picks-up the goods from the various destinations in cyclic routes.

Various mathematical and heuristics models are proposed for finding optimal routes, quantities

and vehicle loads. Most of them are focused on either the vehicle routing, loading or scheduling

individually. Hence, a more common approach is required to achieve the solutions that

effectively balance the vehicle routing, scheduling and loading schemes and can be implemented

for heterogeneous goods. Duygu Yilmaz Eroglu et al [31] have proposed a milk-run

methodology to control the procurement system. The hybrid genetic local search algorithm is

proposed which simultaneously solved vehicle routing and order loading problems. Volume and

weight capacities are taken into account for different types of transportation vehicles to make

the solution more adaptive. Genetic algorithms are the most desired approach of heuristics

method to get the satisfied solution. The chromosome structure of the proposed genetic

algorithm is established by random numbers to abolish infeasibility. The better chromosome of

each generation is developed using local search method. The algorithm is implemented in

Comau S.P., a manufacturing industry that produces welding robots and automation equipment.

The results showed the effectiveness of the algorithm.

Literature Review

26

FIGURE 2.7: Vehicle Routing by Multi-objective Genetic Algorithm [32]

Sangeeta and Sonia Sharma [32] modeled multiple objective genetic algorithm for multi depot

vehicle routing problem with simultaneous pickup and delivery. The proposed algorithm

simultaneous determines the routes for numerous vehicles from multiple depots to a set of

clients and then returning to the origin. Each depot has its own certain capacity and it has been

assumed that the total sum of demand in particular routes to assigned depot will not be more

than its maximum capacity. The objectives of the problem are to find the routes for vehicles to

facilitate all the customers at an optimal cost with the optimization of the total length of routes

and utilization of vehicles without changing the capacity of the vehicles. A multi objective

genetic algorithm is employed to solve the proposed problem with new constraints. The

assignment of stations to a particular origin in multi depots is shown in fig. 2.7. Multi objective

GA is applied on the population and routing of the vehicle is obtained. Matlab is used to run the

algorithm and optimal results are achieved.

Modelling Issues and Heuristic Solution Approaches in VRP

27

2.15 Modelling Issues and Heuristic Solution Approaches in VRP

Vehicle routing problem with different nature of issues are challenging to handle in a practical

situation. Different heuristics and metaheuristics methods can be applied to solve the problem

according to the nature of a problem. However, no exact algorithm can be definitely confirmed

to find an optimal solution within reasonable computing time when the problem is real life and

large scale. Niaz A. Wassan and Gabor Nagy [33] have investigated the different version of

vehicle routing problem including traditional and new one with the difference in terms of

practical approach and modeling issues especially focusing on the assumption made in the

literature and not making a restrictive assumption. The researchers have described the

backhauling aspect of vehicle routing problem, which has practical application in supply chain

industries, especially in reserve logistic. The proposed research work mainly concentrates on

restriction on making an assumption. Authors have investigated two assumptions; single

demands and combined demands. These assumptions have uncovered rise of other problems,

such as VRP with a restricted mixing of deliveries and pick-ups, VRP with divisible deliveries

and pickups, VRP with a restricted mixing of divisible deliveries and pickup etc. Universal

Integer linear programming is formulated, which is adoptable to the range of various problems.

Various solution methodologies including meta-heuristic to solve the problem are discussed.

2.16 Hybrid Meta-Heuristic Algorithm for VRPSPD Problem

In the last few years, the vehicle routing problem with simultaneous pickup and delivery

(VRPSPD) has drawn attention because of its significance in real life applications. The

VRPSPD problem comes across in practice during the daily life. Basically, simple heuristics are

often used to gain initial basic feasible solution and hybrid metaheuristics are implemented

during the search process to obtain the optimal solution. Operation research plays an important

role for the development of metaheuristics for the VRPSPD. Qingguo Jiang et al. [34] have

addressed a vehicle routing problem with simultaneous pickup and delivery service. The

VRPSPD problem is an NP-hard optimization problem; therefore some heuristic models are

unable to obtain an optimal solution for large scale VRPSPD. The authors have developed

hybrid metaheuristics algorithm for the VRPSPD problem. The proposed metaheuristic

approach is a hybridization of two well-known metaheuristics, such as tabu search and simulated

Literature Review

28

annealing. A tabu embedded simulated annealing metaheuristic (TSSA) employed four types of

improvement methods; such as randomly insertion move, swap move, 2-opt move and cross-

exchange move to explore the neighbourhood. The tabu list of prohibited movements is

employed to reduce computation time and overcome local optima encountered of simulated

annealing. The tabu list prohibited revisiting previously visited solutions. The proposed tabu

embedded simulated annealing metaheuristic is employed to test the performance of Montane

and Galvao’s VRPSPD benchmark data with 18 test problems and 100–400 customers.

Computational results demonstrate that TSSA could produce high-quality solutions.

2.17 Simultaneous Pick-Up and Delivery with Decision Support System

Vehicle routing problem involve discovery of a set of fleets routes with optimum cost such that

origin and destination of each route are same, each customer is visited precisely once by one

vehicle in the same route, vehicle capacity should be higher than the total demand of each route

and the time duration of each route (including travel and service times) does not exceed a

predefined time limit. Pick-up and delivery are the two different kinds of demands of each

consumer in vehicle routing problems with pick-up and delivery, involve product delivery from

the depot to consumer and goods pick-up from consumer to depot. VRPPD is classified into

three groups according to nature of delivery and pick up demands as [35],

Delivery first- pick-up second

Mixed pick-ups and deliveries

Simultaneous pick-ups and deliveries.

Simultaneous Pick-Up and Delivery with Decision Support System

29

FIGURE 2.8: Components of VRP in Decision Support System [35]

Operation research’s algorithms have limitation to cope up with the necessity of business

decision. This problem has led to the development of the concept of a decision support system

(DSS) as shown in fig. 2.8. The DSS consists of the combination of data, analysis of models and

tools, and software into one integrated system that can support semi structured or unstructured

decision making. The prime function of DSS is not to make a decision but to assist making a

decision. Cevriye Gencer et al. [35] proposed the DSS to obtain the optimal solution for the

homogeneous and heterogeneous fleet. DSS allows assisting decision-maker to understand the

data about the decision, to make more precise decision options and to obtain a further alternative

solution. DSS VRP2.0 program consists of two interfaces, such as data collection interface and

calculation interface. The system supports to find the shortest routes and to computerize the

routes. Initially data, calculation criteria, delivery and pick-up demand from the depots and

capacity of a vehicle are entered into this interface. Calculation interface performs the

calculation process and presents the result. The result involves the number and length of routes

of the supply network with the information of the total amount of load to be delivered and

picked-up on each route. It also shows the penalty cost and bonus coefficients related to the

particular route. In VRP2.0 software, interfaces are derived from visual basic 6.0 programming

language and calculations are derived via a program which is coded in C++ 6.0 language. DSS

Literature Review

30

enhances the opportunity of making the effective decision options and increases the possibility

of making the right decision in terms of satisfied customers (receive a right product in the

reasonable time limit) and industries (delivery within predefined time with lower cost).

2.18 Milk-Run Problem with Time Windows under Inventory Uncertainty

Milk-run material supply system constructs the route, determine scheduling and number of

vehicles with the primary assumption that all vehicles must return with the collected goods from

the supplier to the depot. This kind of modelling involves different objectives, whether

minimization of transportation cost or minimization of inventory holding cost. The milk-run

problem under uncertain inventory is the excessive risk loss. Lack of supply can ruin

manufacturing plan and excessive stock on an inventory level are also obstacles to management.

The industry has to face an enormous amount of loss if proper care is not taken in both the cases.

The prime goal of inventory management is to make a balance between the minimization of

total cost and maximization of the customer satisfaction. Robust optimization could be

implemented to obtain the optimal solution of optimization problems with data uncertainty.

M. Jafari-Eskandari et al. [19] have proposed a robust optimization approach to solve the milk

run system with time window with inventory uncertainty. This approach constructs routes that

minimize total transportation costs along with the satisfaction of all inventory in a given

bounded uncertainty set. The milk-run material supply system problem has been utilised to

manage the transportation of materials. Since the resulted problem formulation is NP-Hard, in

order to solve the underlying problem, a novel algorithm entitled robust optimization has been

proposed. The robust solution efficiency is compared with deterministic by applying the model

in numerical examples. Particle swarm optimization has also been proposed to solve the problem

in practical large scale. In order to analyse the performance of the proposed method, initially

mixed integer software package was implemented by data from SAIPA for three different

variables: the number of suppliers, the number of parts and the time horizon for planning.

However due to many variables in proposed model, robust optimization approach became

inevitable to solve the problem. It has been seen from the result that robust optimization is an

effective substitute for formulating milk run problem under uncertainty as it does not count on

distribution assumptions about the uncertainty or a cumbersome demonstration through

Effect of Milk-Run Supply System in Global Warming

31

circumstances. The results represent unmet inventory which can be reduced in robust solution

while acquiring a small additional cost over deterministic optimal routes. Although the robust

optimisation solution levies extra cost to the system but unmet inventory can be controlled in

the logistic network.

2.19 Effect of Milk-Run Supply System in Global Warming

An extensive amount of industrial revolution has raised the threat of global warming as human

activity is increasing. The level of carbon emission has been rising extensively since many years.

The response to the environmental problem is a major issue on a global level. Proper steps must

be undertaken to reduce the growing emission in the environment. In the logistics field, the

crucial aim is to decrease the amount of carbon emission, which is exhausted from the vehicles.

The problem of global warming in the environment due to the direct shipment can be controlled

by implementing the milk run logistics. The overall transportation cost can be optimized by

milk-run material supply system in a logistic network.

Gurinder Singh Brar and Gagan Saini [39] have studied the effects of the conventional

transportation on the traffic conditions and environment. It has been observed that the

implementation of milk run logistics can be more effective even under highly dense traffic

conditions. The milk-run process can allow the optimisation of the number of trucks, which

results in the reduction of the vehicle on the road. On the other hand, reduction in the utilisation

of a number of vehicles for supply network leads to the reduction of carbon emission in the

environment. Milk-run supply chain management system enhances the average loading factor

rates in terms of vehicle utilisation at an optimum level, which leads to decrease in the number

of vehicles in a logistic network. It also effectively constructs and schedules the routes to reduce

travel distances. Accordingly, it is an effective method of transport in which exhaust gases from

the vehicle can be reduced. Therefore, Milk-run supply system can be extremely significant

from the environmental point of view. Furthermore, as milk-run is accurate supply chain

management system, it leads to increase in transportation reliability.

Literature Review

32

2.20 Research Gap

It has been observed that many companies around the world aim to implement milk-run material

supply approach though very few have successfully adopted it into the logistic network. Milk-

run supply system studies usually involve formulating a mathematical model of the associated

VRPTWSPD and attempt to solve it using heuristics approach, since it is difficult to get

optimum solution, especially in real world setting. The study of literature review demonstrated

that various heuristic approaches have been attempted by the researchers so far, such as Robust

optimisation approach, Route construction algorithm, Iterated local search, Differential

evolution algorithm, Genetic algorithm, Nearest neighbour method, Multi swarm optimisation

algorithm, Branch and cut algorithm, Variable neighbourhood descent method, Modified saving

algorithm, Simulated annealing, Greedy algorithm, Dynamic programming, Taboo search etc.

However, there is often conflict for selection of an appropriate heuristic model, as it has impact

on optimality of the solution. Thus, there is a considerable amount of studies required for

optimisation of the milk-run supply system. Therefore, the contribution of this research will be

significant to both the industry and the literature.

Study of extensive literature review illustrates that with all the progress in the design of milk-

run material supply systems which have been made over the last few years, it still has some

major challenges, which are described below.

Some automobile industries have adopted milk-run material supply system, though it is

still a challenging issue for other industries to adopt effective milk-run in the logistic

network, such as perishable industries believe more in the direct shipment than a cyclic

route as there will be chances of loss if the product gets melt. Even one of the

disadvantage of milk-run system is that all suppliers cannot implement a milk-run.

The Milk-Run logistics requires accurate management, based on the operational plan. In

case of poor planning the number of extra trips may increase which may lead to

additional transportation cost.

Research Gap

33

In case of, handling split, consolidation or heterogeneous goods may increase the

number of trip that leads to additional transportation cost. It is challenging to achieve

optimal solution in such scenario.

Customer’s waiting time and penalty cost have not been considered in the overall

transportation cost, if delivery does not take place in predefined time period.

Dynamic, fuzzy, and stochastic goods with different processing routes can make

problem more difficult and cumbersome to solve.

It has been observed that when the demand is very small, the pure milk-run strategy can

be used, as for large scale demand, holding cost is high and fixed truck cost is low, the

milk-run strategy is used with the direct shipment. When milk run strategy is used, it

carries only a small quantity of shipment so that the demand can be closely matched or

an additional truck cost can be prevented.

The studies of environmental pollution (amount of carbon emission) issues have not

been observed and calculated, which can be reduced by effective utilization of vehicle.

There are many uncertainties existing in practical scenario, such as delivery reliability,

stochastic customer demand, etc. These uncertainties greatly influence the vehicle

scheduling, which has not been attempted.

There are several open issues that deserve attention to extend VRPSPD problem, which

include more practical considerations, such as time windows, multiple objectives and

travel times to move the problem even closer to real world problems.

Although minimizing total cost is an important criterion but for some services, criteria

such as minimizing customer inconvenience and minimizing response time may be

equally important that has to be accounted.

34

CHAPTER – 3

Necessity, Objective & Scope of Study

3.1 Problem Statement

To address the problem of having route development for milk-run supply system, effective

mathematical model and heuristic approach need to be developed that constructs the routes and

determines the number of vehicles, scheduling and service period of just-in-time basis to

minimize material handling and total inventory holding cost.

3.2 Necessity

Nowadays the industries are experiencing cut throat competition due to globalisation.

International organisations are in competition with the local business, which makes tough for

local industries to sustain. Enormous invention and innovations have been carried out to reduce

the production cost, which has major involvement in total production cost. However,

transportation cost also has a major impact, especially while the product delivery has to be made

for long and isolated distances. The adoption of an optimised transportation system has become

inevitable due to competitive business environment among the industries and higher

expectations of customers. It has also been observed that congestion and variability in demand

and travel times affect industries on four major service dimensions, such as; travel time,

reliability, cost of transport and cost of inventory [19]. These challenges have to be dealt

efficiently to achieve effective supply chain. On the other hand, waiting time directly influences

the satisfaction of the customer; therefore transportation problem becomes multi-objective along

with the transportation cost. One of the crucial issues is exhaust gases of vehicles, which

contains carbon dioxide and carbon monoxide that have an adverse effect on the environment.

Carbon emission enhances the global warming, which is the major problem that the world

Objective of Research Work

35

currently faces. Effective utilisation of the vehicle can reduce the number of vehicles in supply

network by optimised allocation and scheduling.

The critical issue noted in supply industries are:

The competition in business environment is increasing day by day that makes supplier

to employ effective logistic system to reduce the transportation cost as low as possible.

Lean practice requires the adoption of the lean supply chain management for the

foundation of business.

Transportation system has a vital effect on inventory holding cost.

As the traffic congestion is a major issue in urban area and highway, supplier has to

make sure that product delivery takes place in shortest time and distance along with low

cost by determining effective route.

Customer’s reliability and satisfaction must be taken into account among the customer,

which is achieved by delivering a right product in right time and low charges.

Inappropriate utilisation of the vehicle can enhance the number of vehicles in the supply

network.

3.3 Objective of Research Work

The main goal of the study is to investigate the implementation of milk-run material supply

system in local courier industry with simultaneous pick-up and delivery within time limitation

and examine its effects on overall performance and total cost. Our aim is that the conduction of

the research provides optimization of the supply chain network for the development of milk-run

supply system in courier industry as an alternative system. For suppliers and researchers, it is

hoped that the study also contributes to better understanding of inventory management. Besides,

research will contribute to set up feasible routes and offer quickest response to the demand of

the customers.

The overall objectives of the research work are as following:


36

Determine the scheduling of vehicle, period of the pick-up and delivery services and

quantity of vehicles.

Construction of the routes based on lowest time and cost required to travel to facilitate

both distribution and collection.

Optimization of total material handling and inventory holding cost.

To deliver the products on just in time to achieve higher customer satisfaction.

To reduce the carbon dioxide and carbon monoxide emission in the environment by

optimizing vehicles utilisation in the transportation network.

3.4 Scope of Study

The proposed model is focused on building robust optimisation approach for the courier supply

chain network. The model mainly concentrates on the construction of the routes and scheduling

of vehicle to optimize the transportation cost, inventory holding cost and travelling time. The

optimized model can be successfully implemented in the transportation services which involve

simultaneous pick-up and delivery within the certain time limit, such as courier services, grocery

chain services, perishable supply, industrial supply chain management etc.

The scope of the research is defined as:

Conventional material supply system for the courier services has been studied to

understand the previous construction of routes, location of the stations, average demand

and supply on daily basis, scheduling and requirement of a number of vehicles in supply

network.

Mathematical model has been developed based on the application of logistic network of

courier industry.

Genetic algorithm has been proposed to solve the mathematic model.

Routes have been constructed based on milk-run material supply system to optimize

transportation cost and travelling time.

Methodology of Research

37

The number of vehicles is determined for the network, based on the demand of the

consumer with optimized fixed cost.

Scheduling of the vehicles is determined based on the frequency for the reduction of the

penalty cost and inventory holding cost.

User-friendly application for the android handset has been developed for the easy

assessment of the overall system.

The database has been developed based on the previous record to forecast the average

demand of the consumer and determine average travelling time and cost between each

station.

Overall surveillance of the whole network by global positioning system (GPS) located

in each vehicle.

3.5 Methodology of Research

The methodology adopted for the development of milk-run material supply system is described

step by step as below;

1. Conventional material supply system has been studied and transportation cost is

calculated for the courier services.

2. Location of stations, number of routes and vehicles in transportation networks are

studied and analysed.

3. Identification of the quantity of the product to be picked-up and delivered on everyday

basis for each station.

4. Establishment of the routes according to milk- run supply system for the pick-up and

delivery service.

5. The time frame constraint has been accounted for each route for penalty cost.


38

6. Adjustment of the similar routes for the delivery of the product (according to the time

frame).

7. Establishment of the period of delivery and pick up services.

8. Calculation of no. of vehicle need to be required in each route.

9. Scheduling of vehicle on the basis of demand and number of vehicle.

10. Determination of the overall cost (Transportation cost, fixed cost and penalty cost) and

evaluation of the performance of the model.

11. Development and evaluation of milk run alternatives.

12. Implementation of milk runs: Definition of a milk run schedule, conduct supplier

workshops, testing and controlling.

39

CHAPTER – 4

Research Methodology

4.1 Formulation of Mathematical Model

The mathematical model of the milk-run supply system is formulated to analyse the problem,

according to the necessity and application of the logistic chain of courier industry. The

mathematical model helps to explain the system and to study the effect of different parameters

for overall transportation cost.

4.1.1 Assumptions

Following assumptions are made for the formulation of the mathematical model.

The buffer stock at each pickup point should be predefined.

The vehicle assigned to the route must visit each point.

The distance and plan of each route must be known.

The delivery rate of each station should be predetermined.

4.1.2 Objective Function

The objective function of the mathematical model is to minimize the total cost. The first part of

the objective function indicates total transportation cost. The second part explains the total fixed

cost of the vehicle (Initial cost of a vehicle, Taxes, Insurance charges, Vehicle permit charges

etc.) according to the milk-run. The third part shows the penalty cost if the delivery does not

take place in predefined time period.


40

𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑍 = ∑ ∑ ∑ 𝑐𝑖𝑗 𝑥𝑖𝑗𝑘

𝑛

𝑗=0

𝑛

𝑖=0

𝐾

𝑘=1

+ ∑ 𝑐𝑓𝑘 + ∑ 𝑝𝑖 𝑡𝑖

𝑛

𝑖=1

𝐾

𝑘=1

(1)

4.1.3 Constraints

Subject to,

∑ ∑ 𝑥𝑖𝑗𝑘 ≥ 1, 𝑖 = 1,2, … . , 𝑛

𝑛

𝑖=0

(2)

𝐾

𝑘=1

∑ ∑ 𝑥𝑖𝑗𝑘 ≥ 1, 𝑗 = 1,2, … . , 𝑛

𝑛

𝑗=0

(3)

𝐾

𝑘=1

Each Point is visited by minimum one vehicle.

∑ 𝑥𝑖𝑗𝑘 −

𝑛

𝑖=0

∑ 𝑥𝑗𝑖𝑘 = 0, 𝑗 = 0,1, … . , 𝑛 𝑘 = 1,2, … . , 𝐾 (4)

𝑛

𝑖=0

Same vehicle arrives and departs from each point between the node i and j.

𝑋𝑖𝑟 = 𝑋𝑗𝑟 (5)

The related stock point assigned to a same “r” route.

𝑞𝑖 = 𝑡𝑓𝑖𝑥𝑒𝑑 . 𝑋𝑖𝑟 . (6)

The total demand of a point “i” at “r” route is determined.

𝑞𝑖 ≤ 𝑐𝑘, 𝑖𝑓 𝑥𝑖𝑗𝑘 = 1 (7)

The vehicle capacity should be higher than demand.

𝑞𝑖 ≤ 𝑐𝑖𝑘, 𝑖𝑓 𝑥𝑖𝑗𝑘 = 1 (8)

Each vehicle has free capacity at the arrival at point “i”. It should be greater than pick up demand

at that respective point.

𝑏𝑜 ≤ 𝑎𝑖 ≤ 𝑡𝑜 (9)

Arrival time must be within time frame.

𝑤𝑖 = 𝑑𝑖 − (𝑎𝑖 + 𝑠𝑖) (10)

Formulation of Mathematical Model

41

Calculate the waiting time before the departure at point “i”.

4.1.4 Notations

The necessary notations are defined for the formulation of mathematical model, which are

explained in following sections.

Indices

Z = Total cost

k = Vehicle index

t = Time period

i, j = Pick-up and delivery point indexes

n = Set of clients

r = Route index

K = Maximum number of vehicles in distribution centres

Parameter

𝑐𝑘 = Capacity of vehicle “k”

𝑐𝑖𝑘 = Free capacity of vehicle “k” on arrival at node “i”

𝑞𝑖 = Pick-up and delivery demand at point “i”

𝑏𝑜 = Bottom end of the time frame at point “i”

𝑡𝑜 = Top end of the time frame at point “i”

𝑠𝑖 = Service time at point “i”

𝑑𝑜= The output and input ratio of point “i”


42

𝑡𝑓𝑖𝑥𝑒𝑑 = The determined time period

Variable

𝑐𝑖𝑗 = Delivery cost between “i” and “j”

𝑥𝑖𝑗𝑘 = 1 if vehicle “k” departs point “i” to point “j”; 0 otherwise

𝑐𝑓𝑘 = Fixed cost of the vehicle “k”

𝑋𝑖𝑟 = Point “i” is assigned or not assigned to route “r” (1 or 0)

𝑋𝑗𝑟 = Point “j” is assigned or not assigned to route “r” (1 or 0)

𝑎𝑖 = Arrival time at node “i”

𝑤𝑖 = Waiting time before departure

𝑑𝑖 = Departure time from point “i”

𝑝𝑖 = Penalty cost per unit time at point “i”

𝑡𝑖 = Tardiness for point “i”

4.2 Heuristic Approach

As explained earlier, the design of the milk-run material supply system requires the construction

of routes, scheduling of vehicle and determination of the service period. Mathematical model is

typically formulated and attempted to be solved by heuristics approach for the route

construction. The proposed mathematical model of the JITVRPSPD is nonlinear due to the

continuous large scale demand for each station. Vaidyanathan et al. [4] described that the

problem size increases exponentially with the increase in a number of vehicles and stations that

makes even small scale problem difficult to solve. Hence, a heuristic approach is used to reach

at a close to the optimal solution, through short computational times. The assumptions of the

mathematical model are valid also for the proposed heuristic approach.

Tabu Search Algorithm

43

Initially, four various approaches (Tabu search algorithm, Prim algorithm, Simulated annealing

and Genetic algorithm) are tested by considering the small region of the problem to find an

optimal solution. Mehsana zone of Gujarat state is considered to compare the optimal result of

each algorithm. Table 4.1 shows the comparison of various approaches for the milk-run material

supply system.

TABLE 4.1 Comparison of Various Algorithms for Milk-Run Supply System

Tabu Search

Algorithm

Simulated

Annealing

Prim Algorithm

Genetic

Algorithm

Route 1 3440 3440 1932 1840

Route 2 4030 3790 2887 2655

Route 3 6710 6450 5092 4570

Total Cost (Rs.) 14180 13680 9911 9065

The optimal solution achieved by each algorithm is evaluated and compared with each other to

choose the most suitable algorithm for the concerned problem. Out of the four different

algorithms, genetic algorithm is found to be more suitable and accurate for the nature of our

problem. Based on the comparison, genetic algorithm is selected to study and analyse the entire

problem. The methodology and flowchart of each algorithm are explained in following sections.

4.2.1 Tabu Search Algorithm

Tabu search is a metaheuristic search approach for solving a mathematical or combinatorial

optimization problem. It employs local search procedure or neighbourhood procedure to achieve

an improved solution. Local search procedure has a limitation to stick to subregions that can be

optimised by carefully exploring each neighbourhood solution and determined through memory

structure by tabu search. The flow chart of the tabu search algorithm is shown in fig 4.1 and the

methodology is explained in below section.


44

Methodology

Generate initial solution.

Generate neighbouring solution.

Each neighbouring solution is measured.

Explore solution region.

Aspiration criteria (Global minima).

Flow Chart

FIGURE 4.1: Flow Chart of Tabu Search

Evaluate solution

Choose best

admissible solution

Optimal Solution

Update Tabu and

aspiration

conditions

Initialize solution

Create list of

solution

Stopping

criteria

satisfied

End

Simulated Annealing

45

4.2.2 Simulated Annealing

Simulated annealing is a metaheuristic technique to find a good solution (may not be optimum).

It is a method for the approximate global optimisation of given function. For the problem where

an accurate solution is not important than finding an acceptable local optimum, simulated

annealing is most preferable. It is the fastest tool to find the local optimum, but it cannot get

global optimum. The flow chart of simulated annealing is shown in fig. 4.2.

Methodology

Generate initial solution.

Determine control parameters.

If the final solution is not reached.

Randomised test is undertaken.

Downhill and uphill moves on the basis of feasible solution.


Generate frozen state (Final solution).


46

Flow Chart

FIGURE 4.2: Flow Chart of Simulated Annealing

(Source: http://dx.doi.org/10.5772/51126.pdf)

4.2.3 Prim Algorithm

The prim algorithm is also called as a greedy algorithm that discovers a minimum spanning tree

for a connected weighted undirected graph. It finds a subset of the edges that forms a tree which

includes every vertex, where the total weight of all the edges in the tree is minimized. Initially,

the algorithm builds tree by one vertex at a time from an arbitrary starting vertex. It adds the

Prim Algorithm

47

minimum possible connection in each step from the tree to another vertex. The fig. 4.3 represents

the flow chart of prime algorithm.

Methodology

Generate basic feasible solution.


Generate weight on the basis of available feasible solution.

Removal of the loops and parallel edges between the vertexes.

Formation residual table.

Generate optimum solution on the basis of TSP.


48

Flow Chart

FIGURE 4.3: Flow Chart of Prim Algorithm

(Source: http://www.ijcat.com/archives/volume5/issue2/ijcatr05021010.pdf)

Genetic Algorithm

49

4.2.4 Genetic Algorithm

A genetic algorithm is a metaheuristic approach to solve mathematical optimization problems

based on a natural selection process that is based on biological evolution theory. The algorithm

repeatedly modifies individual solutions by producing the children for the next generation of

their parents called current generation. Genetic algorithm generates optimised solutions by

relying on bio-inspired operators such as mutation, crossover and selection. Over successive

generations, the population "evolves" toward an optimal solution. The flowchart of the genetic

algorithm is shown in fig. 4.4.

Methodology

It scores each member of the current population by computing its fitness value.

Parents are selected based on their fitness.

Some of the individuals in the current population have lower fitness value, which are

chosen as elite. These elite individuals are passed to the next population.

Children are produced from the parents of current generation. These children are formed

either by making random changes to a single parent (mutation) or by combining the

vector entries of a pair of parents (crossover).

New produced generation replaces the current population with the children to form the

next generation.


50

Flow chart

FIGURE 4.4: Flow Chart of Genetic Algorithm

4.3 Validation

The validation of the proposed model has been carried out based on the routes developed by

milk-run material supply system. The transportation cost of the existing material supply system

is compared with the cost of the milk-run material supply system. Mehsana zone, which is

located in Mehsana district and Gujarat state is considered for the purpose of the validation

process.

Gen = 0

Create initial random population

Designate best chromosome

found so far as a solution

Yes

Evaluate fitness of each individual in population

Reproduction

Crossover

Mutation

Gen = Gen + 1

If Gen=N

End

No

Existing Material Supply System

51

4.3.1 Existing Material Supply System

The courier service currently practices seven routes to manage the supply in north Gujarat

region. The routes and its distance are specified below in the table.

TABLE 4.2: Existing Material Supply System

Sr. No. Route Distance (km.)

Route : 1 Mehsana-Ahmedabad 140

Route : 2 Mehsana- Bechraji 60

Route : 3 Mehsana- Gojariya 40

Route : 4 Mehsana- Visnagar- Vadnagar- Kheralu- Satalasana 140

Route : 5 Mehsana-Patan 100

Route : 6 Mehsana- Chansma- Harij- sami- Radhanpur-

Bhabhar- Diyodar- Vav- Tharad

300

Route : 7 Mehsana-Unjha- Sidhpur- Palanpur- Deesa-

Dhanera

240

4.3.2 Milk-run Material Supply System

The existing routes are redefined by the concept of the milk-run material supply system and

specified in below tables. The route followed the cyclic manner and pick-up and delivery takes

place during the visit of each station.

TABLE 4.3: Milk-run Material Supply System

Sr. No. Route Distance (km.)

Route : 1 Mehsana- Bechraji- Ahmedabad-Mehsana 225

Route : 2 Mehsana- Gozariya- Visnagar- Vadnagar- Kheralu-

Satlasana- Palanpur- Sidhpur- Unjha- Mehsana

240

Route : 3 Mehsana- Chansma- Harij- Sami- Radhanpur-

Bhabhar- Diyodar- Vav- Tharad- Dhanera- Deesa-

Patan- Mehsana

390


52

The travelling salesman problem solver and the generator are used to arrange the sequence of

each station and calculate the transportations cost. The calculations are shown below for each

route, which are formed according to the milk-run material supply system.

Route: 1

Variant #1 Task

Task:

--- 320 600

320 --- 800

600 800 ---

Variant #1 Solution

Step #1

--- 0 0

0 --- 200

0 200 ---

Selected route with (1;2) part.

3 alternate candidates for branching: (1;3), (2;1),

(3;1).

Step #2

--- --- ---

--- --- 0

0 --- ---

Milk-run Material Supply System

53


1 alternate candidate for branching: (3;1).

Optimal path:

City 1 -> City 2 -> City 3 -> City 1

The price is 1720 units.

Route: 2

Variant #2 Task

Task:

--- 200 160 280 400 610 560 320 240

200 --- 200 320 440 600 760 520 400

160 200 --- 120 24 440 560 320 200

280 320 120 --- 120 320 465 320 300

400 440 240 120 --- 200 360 240 255

610 600 440 320 200 --- 440 440 440

560 760 560 465 360 440 --- 240 385

320 520 320 320 240 440 240 --- 135

240 400 200 300 255 440 385 135 ---

Variant #2 Solution

Step #1

--- 0 0 120 240 370 295 160 80

0 --- 0 120 240 320 455 320 200


54

136 136 --- 96 0 336 431 296 176

160 160 0 --- 0 120 240 200 180

280 280 120 0 --- 0 135 120 135

410 360 240 120 0 --- 135 240 240

320 480 320 225 120 120 --- 0 145

185 345 185 185 105 225 0 --- 0

105 225 65 165 120 225 145 0 ---


Step #2

--- --- --- --- --- --- --- --- ---

--- --- 0 120 240 320 455 320 200

136 --- --- 96 0 336 431 296 176

160 --- 0 --- 0 120 240 200 180

280 --- 120 0 --- 0 135 120 135

410 --- 240 120 0 --- 135 240 240

320 --- 320 225 120 120 --- 0 145

185 --- 185 185 105 225 0 --- 0

105 --- 65 165 120 225 145 0 ---



Step #3


55

--- --- --- --- --- --- --- --- ---

--- --- 0 120 240 320 --- 320 65

31 --- --- 96 0 336 --- 296 41

55 --- 0 --- 0 120 --- 200 45

175 --- 120 0 --- 0 --- 120 0

305 --- 240 120 0 --- --- 240 105

215 --- 320 225 120 120 --- --- 10

--- --- --- --- --- --- --- --- ---

0 --- 65 165 120 225 --- 0 ---



Step #4

--- --- --- --- --- --- --- --- ---

--- --- 0 24 240 --- --- 320 65

31 --- --- 0 0 --- --- 296 41

55 --- 0 --- 0 --- --- 200 45

--- --- --- --- --- --- --- --- ---

305 --- 240 24 --- --- --- 240 105

205 --- 310 119 110 --- --- --- 0

--- --- --- --- --- --- --- --- ---

0 --- 65 69 120 --- --- 0 ---


56


Step #5

--- --- --- --- --- --- --- --- ---

--- --- 0 24 240 --- --- --- 24

0 --- --- 0 0 --- --- --- 0

24 --- 0 --- 0 --- --- --- 4

--- --- --- --- --- --- --- --- ---

250 --- 216 0 --- --- --- --- 40

64 --- 200 9 0 --- --- --- ---

--- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- ---


Step #6

--- --- --- --- --- --- --- --- ---

--- --- 0 --- 240 --- --- --- 24

0 --- --- --- 0 --- --- --- 0

24 --- 0 --- 0 --- --- --- 4

--- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- ---

64 --- 200 --- 0 --- --- --- ---

--- --- --- --- --- --- --- --- ---


57

--- --- --- --- --- --- --- --- ---


Step #7

--- --- --- --- --- --- --- --- ---

--- --- 0 --- --- --- --- --- 24

0 --- --- --- --- --- --- --- 0

24 --- 0 --- --- --- --- --- 4

--- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- ---



2

Step #8

--- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- 0

0 --- --- --- --- --- --- --- 0

--- --- --- --- --- --- --- --- ---


58

--- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- ---



Resulting path:

City 1 -> City 2 -> City 3 -> City 9 -> City 8 -> City 7 -> City 5 -> City 6-> City 4 -> City 1


Route: 3

Variant #3 Task

Task:

--- 280 465 585 780 1010 905 1330 1250 1030 755 435

280 --- 190 310 510 735 630 1025 1080 815 545 140

465 190 --- 130 330 555 450 840 905 945 750 250

585 310 130 --- 230 455 570 740 800 1065 365 375

780 510 330 230 --- 260 375 545 605 905 705 470

1010 735 555 455 260 --- 165 285 350 700 575 650

905 630 450 570 375 165 --- 445 385 610 415 495

1330 1025 840 740 545 285 445 --- 100 465 575 935

1250 1080 905 800 605 350 385 100 --- 370 495 880


59

1030 815 945 1065 905 700 610 465 370 --- 275 685

755 545 750 365 705 575 415 575 495 275 --- 410

435 140 250 375 470 650 495 935 880 685 410 ---

Variant #3 Solution

Step #1

--- 0 185 305 405 730 625 1050 970 750 475 155

0 --- 50 170 275 595 490 885 940 675 405 0

195 60 --- 0 105 425 320 710 775 815 620 120

315 180 0 --- 5 325 440 610 670 935 235 245

410 280 100 0 --- 30 145 315 375 675 475 240

705 570 390 290 0 --- 0 120 185 535 410 485

600 465 285 405 115 0 --- 280 220 445 250 330

1090 925 740 640 350 185 345 --- 0 365 475 835

1010 980 805 700 410 250 285 0 --- 270 395 780

615 540 670 790 535 425 335 190 95 --- 0 410

340 270 475 90 335 300 140 300 220 0 --- 135

155 0 110 235 235 510 355 795 740 545 270 ---


Step #2

--- 0 185 305 405 730 625 --- 970 750 475 155

0 --- 50 170 275 595 490 --- 940 675 405 0


60

195 60 --- 0 105 425 320 --- 775 815 620 120

315 180 0 --- 5 325 440 --- 670 935 235 245

410 280 100 0 --- 30 145 --- 375 675 475 240

705 570 390 290 0 --- 0 --- 185 535 410 485

600 465 285 405 115 0 --- --- 220 445 250 330

1090 925 740 640 350 185 345 --- --- 365 475 835

--- --- --- --- --- --- --- --- --- --- --- ---

615 540 670 790 535 425 335 --- 95 --- 0 410

340 270 475 90 335 300 140 --- 220 0 --- 135

155 0 110 235 235 510 355 --- 740 545 270 ---


Step #3

--- 0 185 305 405 730 625 --- 875 --- 475 155

0 --- 50 170 275 595 490 --- 845 --- 405 0

195 60 --- 0 105 425 320 --- 680 --- 620 120

315 180 0 --- 5 325 440 --- 575 --- 235 245

410 280 100 0 --- 30 145 --- 280 --- 475 240

705 570 390 290 0 --- 0 --- 90 --- 410 485

600 465 285 405 115 0 --- --- 125 --- 250 330

905 740 555 455 165 0 160 --- --- --- 290 650

--- --- --- --- --- --- --- --- --- --- --- ---


61

615 540 670 790 535 425 335 --- 0 --- --- 410

--- --- --- --- --- --- --- --- --- --- --- ---

155 0 110 235 235 510 355 --- 645 --- 270 ---


Step #4

--- 0 185 305 405 730 625 --- --- --- 240 155

0 --- 50 170 275 595 490 --- --- --- 170 0

195 60 --- 0 105 425 320 --- --- --- 385 120

315 180 0 --- 5 325 440 --- --- --- 0 245

410 280 100 0 --- 30 145 --- --- --- 240 240

705 570 390 290 0 --- 0 --- --- --- 175 485

600 465 285 405 115 0 --- --- --- --- 15 330

905 740 555 455 165 0 160 --- --- --- 55 650

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

155 0 110 235 235 510 355 --- --- --- 35 ---



Step #5

--- --- --- --- --- --- --- --- --- --- --- ---


62

--- --- 50 170 275 595 490 --- --- --- 170 0

195 --- --- 0 105 425 320 --- --- --- 385 120

315 --- 0 --- 5 325 440 --- --- --- 0 245

410 --- 100 0 --- 30 145 --- --- --- 240 240

705 --- 390 290 0 --- 0 --- --- --- 175 485

600 --- 285 405 115 0 --- --- --- --- 15 330

905 --- 555 455 165 0 160 --- --- --- 55 650

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

120 --- 75 200 200 475 320 --- --- --- 0 ---


Step #6

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

75 --- --- 0 105 425 320 --- --- --- 385 ---

195 --- 0 --- 5 325 440 --- --- --- 0 ---

290 --- 100 0 --- 30 145 --- --- --- 240 ---

2

585 --- 390 290 0 --- 0 --- --- --- 175 ---

480 --- 285 405 115 0 --- --- --- --- 15 ---


63

785 --- 555 455 165 0 160 --- --- --- 55 ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

0 --- 75 200 200 475 320 --- --- --- 0 ---


Step #7

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

75 --- --- 0 100 425 --- --- --- --- 385 ---

195 --- 0 --- 0 325 --- --- --- --- 0 ---

290 --- 100 0 --- 30 --- --- --- --- 240 ---

--- --- --- --- --- --- --- --- --- --- --- ---

480 --- 285 405 110 --- --- --- --- --- 15 ---

785 --- 555 455 160 0 --- --- --- --- 55 ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

0 --- 75 200 195 475 --- --- --- --- 0 ---


Step #8


64

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

75 --- --- 0 --- 425 --- --- --- --- 385 ---

--- --- --- --- --- --- --- --- --- --- --- ---

290 --- 25 --- --- 30 --- --- --- --- 240 ---

--- --- --- --- --- --- --- --- --- --- --- ---

465 --- 195 390 --- --- --- --- --- --- 0 ---

785 --- 480 455 --- 0 --- --- --- --- 55 ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

0 --- 0 200 --- 475 --- --- --- --- 0 ---


Step #9

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

0 --- 0 --- --- 5 --- --- --- --- 215 ---

--- --- --- --- --- --- --- --- --- --- --- ---

200 --- 195 --- --- --- --- --- --- --- 0 ---


65

520 --- 480 --- --- 0 --- --- --- --- 55 ---

--- --- --- --- --- --- --- --- --- --- --- ---

3

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- 0 --- --- 475 --- --- --- --- 0 ---


Step #10

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- 195 --- --- --- --- --- --- --- 0 ---

--- --- 480 --- --- 0 --- --- --- --- 55 ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- 0 --- --- 475 --- --- --- --- 0 ---



66

Step #11

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- 0 --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- --- --- --- --- --- --- --- --- --- ---

--- --- 0 --- --- --- --- --- --- --- 0 ---



Resulting path:

City 1 -> City 2 -> City 12 -> City 11 -> City 10 -> City 9 -> City 8 -> City 6 -> City 7 ->

City 3 -> City 4 -> City 5 -> City 1



67

TABLE 4.4 Comparison of Existing and Milk-Run Supply System

Sr. No. Existing Supply

System (km.)

Milk-Run Supply

System (km.)

Route 1 140 225

Route 2 60 240

Route 3 40 390

Route 4 140

Route 5 100

Route 6 300

Route 7 240

Total Distance 1020 855

Transportation Cost

(Rs.) 8160 6840

Table 4.3 shows the comparison of the transportation cost between the existing material supply

system and milk-run material supply system. It has been observed that the implementation of

the milk-run material supply system in existing logistic services can have reduction in the

transportation cost to a greater extent.

68

CHAPTER – 5

Result and Discussion

A genetic algorithm is applied to resolve the current routing problem in the courier industry

using the milk-run material supply system. Input data is used to solve the problem;

Matrix of distance, position and travelling time of each station zone wise

Demand data of each station (Volume per cycle) with the cycle

5.1 Development of Routes

The routes have been constructed by genetic algorithm in Matlab based on the geographical

location and distance between each station. The algorithm performed using a graphic user

interface to display the routes, scheduling, total cost, distance and time of travelling of each

zone. The Gujarat state is considered for solving the vehicle routing problem in courier industry.

It is basically divided into five different zones based on the location of the regional offices i.e.

Mehsana, Ahmedabad, Rajkot, Vadodara and Surat. Each zonal office covers the nearby stations

or area. The routes are developed in a cyclic manner based on milk-run material supply system

for various zones. Fig. 5.1 shows the map of Gujarat with all major cities. The location of five

regional offices covers almost all major area of the state.

Development of Routes

69

FIGURE 5.1: Map of Gujarat State

5.1.1 Mehsana Zone

Mehsana regional office covers the area of north Gujarat. It distributes and collects goods from

20 cities around the northern area of the state. A couple of routes are developed by the genetic

algorithm based on a location of stations. Following fig. 5.2 to fig. 5.4 show the routes, total

cost, scheduling of vehicles, travelling distance and travelling time.


70

FIGURE 5.2: Scheduling and Routing for Route 1 for Mehsana Zone

FIGURE 5.3: Scheduling and Routing for Route 2 for Mehsana Zone


71

FIGURE 5.4: Scheduling and Routing for Mehsana Zone

5.1.2 Ahmedabad Zone

Ahmedabad regional office is a centre point of Gujarat state. It is one of the busiest stations of

state. It covers the area of middle Gujarat and Kutch region. It runs service to 29 cities around

the middle-west area of the state. Three routes are developed by the genetic algorithm based on

the location of stations. Following fig. 5.5 to fig. 5.8 show all three routes, total cost, scheduling

of vehicles, travelling distance and travelling time.


72

FIGURE 5.5: Scheduling and Routing for Route 1 for Ahmedabad Zone



73


FIGURE 5.8: Scheduling and Routing for Ahmedabad Zone


74

5.1.3 Rajkot Zone

Rajkot regional office covers the area of Saurashtra region. It has to go through the longest

travelling distance. It serves in 27 cities around the southern-east part of the state. Four routes

are developed by the genetic algorithm based on the location of stations. Following fig. 5.9 to

fig. 5.13 show the routes, total cost, scheduling of vehicles, travelling distance and travelling

time.

FIGURE 5.9: Scheduling and Routing for Route 1 for Rajkot Zone


75




76


FIGURE 5.13: Scheduling and Routing for Rajkot Zone


77

5.1.4 Vadodara Zone

Vadodara regional office covers the area of south Gujarat. It runs service to 16 cities in the

southern part of the state. Two routes are developed by the genetic algorithm based on the

location of stations. Following fig. 5.14 to fig. 5.16 show the routes, total cost, scheduling of

vehicles, travelling distance and travelling time.

FIGURE 5.14: Scheduling and Routing for Route 1 for Vadodara Zone


78

FIGURE 5.15: Scheduling and Routing for Route 2 for Vadodara Zone

FIGURE 5.16: Scheduling and Routing for Vadodara Zone


79

5.1.5 Surat Zone

Surat regional office covers the area of south Gujarat. It distributes and collects the goods from

10 cities across the southern part of the state. Two routes are developed by the genetic algorithm

based on the location of stations. Following fig. 5.17 to fig. 5.19 show the routes, total cost,

scheduling of vehicles, travelling distance and travelling time.

FIGURE 5.17: Scheduling and Routing for Route 1 for Surat Zone


80

FIGURE 5.18: Scheduling and Routing for Route 2 for Surat Zone

FIGURE 5.19: Scheduling and Routing for Surat Zone


81

5.1.6 Gujarat Region

Gujarat state has been divided into five major zones. The routes are developed zone wise based

on the location and distance of the each station. Total 17 routes are developed that covers 110

cities, which almost cover the whole Gujarat state. Fig. 5.20 shows the routes and location of

the regional offices of the Gujarat.

FIGURE 5.20: Scheduling and Routing of Gujarat Region

Based on the output of the program, Table 5.1 shows comparison of conventional supply system

and the milk-run material supply system in terms of total travelling distance and transportation

cost per day for supplier in the entire Gujarat region. The current transportation system has a

total travelling distance and transportation cost which is 7435 km. and Rs. 59480 per day in

Gujarat state respectively. It is way too high as compared to the optimal total travelling distance

and transportation cost which is 5555 km. and Rs. 48730 per day respectively by using the milk-

run material system. It can be concluded from the below table that milk-run supply system

optimize the travelling distance by 25.28 % or 1880 km. in a day and total transportation cost

as Rs. 10750 or 18.07 % per day.


82

TABLE 5.1: Comparison of Conventional Supply System and Milk-Run Material Supply System (Distance

and Overall Cost)

In linear correlation between travelling distance and travelling time, Table 5.2 represents the

reduction in total travelling time. It can be seen from the below table that the milk-run supply

system can optimize the travelling time by 24.40 % in a day in all zones. According to the

current material supply system, the total travelling time is 184.20 hrs., which can be reduced to

139.25 hrs. by successfully implementing milk-run material supply system.

Sr. No. Conventional supply

system (km.)

Milk-run supply

system (km.)

Mehsana Zone 1020 643

Ahmedabad Zone 2000 1753

Rajkot Zone 2580 2047

Vadodara Zone 965 611

Surat Zone 870 501

Total Distance 7435 5555

Transportation Cost

(Rs./Day) 59480 48730

Express Service

83

TABLE 5.2: Comparison of Conventional Supply System and Milk-Run Material Supply System (Travelling

Time)

5.2 Express Service

The provision for express service delivery is also employed in the proposed milk-run material

supply system, if total load is equivalent to the capacity of a vehicle, which needs to be delivered

from one station to another destination. Some of the examples have been shown in fig. 5.21 to

fig. 5.23. The graphical user interface represents the travelling time, distance, detail map and

total transportation cost of one station to another if full vehicle’s capacity load is distributed. As

it requires the origin station name, destination station name and number of travelling vehicle for

the respective route as an input data, the route is developed by the help of genetic algorithm in

Matlab based origin and destination. Total cost, travelling time and distance are calculated by

Matlab based on each route.

Sr. No. Conventional supply

system (Time)

Milk-run supply

system (Time)

Mehsana Zone 26.40 Hrs. 18.55 Hrs.

Ahmedabad Zone 49.10 Hrs. 40.10 Hrs.

Rajkot Zone 60.10 Hrs. 50.10 Hrs.

Vadodara Zone 28.20 Hrs. 19.05 Hrs.

Surat Zone 20.40 Hrs. 11.45 Hrs.

Total 184.20 Hrs. 139.25 Hrs.


84

FIGURE 5.21: Express Service from Unjha to Vapi

FIGURE 5.22: Express Service from Rapar to Godhara

CO2 Emission

85

FIGURE 5.23: Express Service from Junagadh to Gandhinagar

5.3 CO2 Emission

The problem of air pollution has become a global issue. As the technology is growing rapidly,

the fatal diseases in human being are increasing which includes lung cancer due to the polluted

air. It also contributes to smog, acid rain and damage to other living organisms such as birds,

animals and food crops. This research optimised the vehicle’s utilisation rate and distance by

constructing the cyclic routes instead of direct shipment. The result indicates improvement in

utilisation of vehicle and shortens the distance by implementing simultaneous pick-up and

delivery in a cyclic manner. The gas emission in the environment can be calculated from the

given formula also the GUI to calculate the CO2 emission of Gujarat is developed, which is

shown in fig. 5.24. The aim of this research work is to achieve effective utilisation of the vehicle

and reduction of overall carbon emission in the environment.


86

CO2 EM = VH × MI × D × EF (11)

where,

CO2 EM = CO2 emission in (kg)

VH = Number of Vehicle

MI = Mileage of vehicle (Km/Lt)

D = Distance travelled by vehicle (km)

EF = Emission Factor (Kg/Lt)

FIGURE 5.24: CO2 Emission of Gujarat Region

Vehicle Routing Database

87

5.4 Vehicle Routing Database

The android application has been developed to track the vehicle throughout the route. This

application will be installed and run into each station of the respective route for supervision and

inspection purpose. It will be also installed into the mobile handset of a driver to make sure that

the data input is authentic and correct. It will indicate the detailed schedule and weight

distribution (Both pick-up and delivery) of the entire route. The analysis was made through the

available database, which will indicate the weight distribution across the logistic network. Fig.

5.25 shows the input page of the application, through which data entry can be done.

FIGURE 5.25: Vehicle Routing Android Application


88

FIGURE 5.26: Vehicle Routing Weight Analysis

This android application is run as a trial testing to obtain the database for one week. The prepared

database will be used for the future reference to forecast the demand and supply, which will

support to determine the number of vehicles and scheduling of vehicles required in each route.

Based on the trial testing the database is prepared and analyzed in Matlab by using the graphical

user interface to determine the weight distribution in entire Gujarat state, which is shown in fig.

5.26. It will also indicate the travelling time required between two stations and driver’s detail of

the delivery took place in the past. The above fig. 5.26 shows the amount of weight carried

between each station. The red line indicates the weight distribution taken place above 750 kg.

between two stations. Likewise, orange line, yellow line and green line represent the weight

distribution between 350 kg. to 750 kg., 175 kg. to 350 kg. and below 175 kg. respectively. It

has been observed from the fig. 5.26 that the ratio of weight distribution is higher between major

cities and regional offices as compared to the small cities and isolated places.

Red: Above 750 kg.

Orange: Between 350 kg.

to 750 kg.

Yellow: Between 175 kg.

to 350 kg.

Green: Below 175 kg.

Development of Routes with Uncertain Demand

89

5.5 Development of Routes with Uncertain Demand

There are many uncertainties existing in the practical environment, such as delivery reliability,

stochastic customer demand, dynamic environment etc. These uncertainties are having influence

on vehicle scheduling and transportation cost. The demand and supply highly depend on the

market scenario. It varies according to the seasons, customer’s nature, festivals and various

regions. In supply chain management, demand of the consumer impact on the transportation

system extremely. Transportation cost, no. of vehicles and scheduling depends on the demand

and supply to a greater extent. Uncertain and stochastic demand may lead to additional

transportation cost and dissatisfaction of customer. In such kind of situation, it is inevitable to

develop the model, which has an ability to form the routes according to the necessity and

optimize the transportation cost and travelling time.

The genetic algorithm has been modified to overcome the issues of the dynamic environment.

Mehsana zone (Route: 1) has been considered to demonstrate the model with uncertain demand.

It has 9 stations, which are listed below.

Mehsana

Gozaria

Visnagar

Vadnagar

Kheralu

Satlasana

Palanpur

Sidhpur

Unjha

Mehsana

Initially, the route is developed with the full demand of each station. Fig. 5.27 shows the

development of routes with an uncertain environment (Case: 1) with demand at each station. It

has been noticed that the distance, travelling time and cost are 241 km., 7 hr. 25 min., Rs. 2258

respectively with demand at each station.


90

FIGURE 5.27: Development of Routes with Uncertain Demand (Case: 1)

Fig. 5.28 shows the development of routes with an uncertainty in demand (Case: 2). In the

simulation of second case the demand of two stations (Vadnagar and Satlasana) are considered

to be zero. The genetic algorithm has developed a new route according to the demand of each

station. It has formed the route by removing theses stations (Vadnagar and Satlasana) from the

network, which have different travelling distance, time and cost as compared to previous case.

It can be seen from fig. 5.28 that the distance, travelling time and cost are reduced to 202 km.,

6 hr. 15 min., Rs. 1946 respectively.

Development of Routes with Uncertain Demand

91


The modified genetic algorithm has tested one more time by considering the zero demand of

any other stations, which is shown in fig. 5.29 (Case: 3). The demand of Gozaria and Satlasana

are considered to be zero in another trial. According to the demand of each station, the route is

developed by an algorithm, which has different value than previous cases. It has been observed

that the distance, travelling time and cost is changed to 212 km., 6 hr. 45 min., Rs. 2016

respectively with demand at each station except Gozaria and Satlasana (Case: 3).


92


It has been observed through above cases that the modified genetic algorithm can be employed

when the demand is uncertain. It has an ability to form the routes according to the demand of

each station. The algorithm can take out the stations from the route whose demand is nil and

construct the optimized route accordingly.

93

CHAPTER – 6

Conclusion and Future Scope

The Gujarat state is considered to study and implement the milk-run material supply system.

The vehicle routing problem in supply chain management has been studied for courier industry,

as it has a major involvement in the logistic industry. The mathematical model is formulated

and solved by heuristics approach for the route construction. A genetic algorithm is employed

in Matlab to solve the current travelling salesman problem. Gujarat has 196,024 km2 of total

area, which has been divided into five zones. According to the concept of the milk-run material

supply system total seventeen routes are constructed, which covers 110 cities across Gujarat.

It has been observed from the result that the overall cost, time and distance is significantly

reduced by implementing the milk-run material supply system in courier industry.

6.1 Conclusion

The following points can be concluded from the results;

In this study, the mathematical formulation and genetic algorithm is proposed for

homogeneous and heterogeneous fleets for VRPTWSPD encountered in real life

situations. In this research work, routes are constructed, service and delivery periods are

determined and schedule is obtained. Computational results and proposed procedure

represent an excellent performance.

The overall supply chain cost has minimized by implementing the milk-run system in

transportation network instead of direct shipment. It is seen from the results that the

overall cost is reduced by 18.07 % in a day, which is Rs. 10750 less per day as compared

to direct shipment.

Conclusion and Future Scope

94

The result shows the reduction in the carbon dioxide emission from the transportation

system by improving the vehicle utilization. The overall reduction of CO2 emission is

24.94 % per day in the environment.

The overall distance of the constructed routes is lower than the previous system. It is

seen from the result that vehicle’s travelling distance is decreased to 1880 km. per day,

which is 25.28 % less as compared to the conventional material supply system.

With the linear connection between vehicle’s travelling distance and travelling time, the

reduction of vehicle’s travelling time is calculated as 44.95 hrs. per day, which is

optimized by 24.40 %.

The model has also modified for uncertain and stochastic demand.

Hence implementation of the milk-run supply system can compress cycle time, minimize

distance, decrease total cost, reduce carbon emission, increase vehicle utilization and

improve profitability of the industry.

6.2 Future Scope

Future research will be mainly addressed towards extending the application of the

algorithm to various manufacturing environment to test its suitability and foster the

necessary improvement.

95

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11. Boysen N, Bock S (2010) Scheduling just-in-time part supply for mixed-model

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12. Costa B, Dias LD, Oliveira JA, Pereira G (2008) Simulation as a tool for planning a

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13. Angelelli E, Mansini R (2002) The vehicle routing problem with time windows and

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15. Satoglu S, Sahin E (2012) Design of JIT periodic material supply system for the

assembly line and an application in electronics industry. International journal of

advanced manufacturing technology. DOI 10.1007/s00170-012-4171-7.

16. Nemoto T, Hayashi K, Hashimoto M (2010) Milk-run logistics by Japanese

automobile manufacturers in Thailand. Proc Soc Behav Sci 2, pp. 5980–5989.

17. Kilic HS, Durmusoglum MB, Baskak M (2012) Classification and modelling for in-

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27. Liang Chun-Hua, Zhou Hong, Zhao Jian (2009) Vehicle routing problem with time

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routing problem with simultaneous pick-up and deliveries. Computers & industrial

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34. Qingguo Jiang, Chao Wang, Dong Mu, Lingyun Zhou (2015) A hybrid metaheuristic

algorithm for the VRPSPD problem. Journal of computational information systems

11: 13, pp. 4845–4856.

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saddle river, NJ, pp. 476–497.

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and modeling for in-plant milk-run distribution systems. International journal of

advanced manufacturing technology. DOI 10.1007/s00170-011-3875-4.

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milk-runs for stochastic vehicle routing, research showcase, Carnegie mellon

university.

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39. Gurinder Singh Brar, Gagan Saini (2011) Milk run logistics: literature review and

directions, Proceedings of the world congress on engineering 2011, Vol I, WCE

2011, July 6 - 8, 2011, London, U.K.

40. Pacharawan Suebsangin, Naruemol Ratanamalakul, Aussadavut Dumrongsiri

(2013) Planning of production, inventory and logistics with direct shipment and milk

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of JCB India Ltd. (Ballabhgarh Plant). Journal of traffic and logistics engineering,

Vol, 1, No. 2 December 2013.

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publishing corporation mathematical problems in engineering, Volume 2014, Article

ID 536459, 6 pages.

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algorithms for a vehicle routing problem with simultaneous delivery and pickup and

time windows in home health care, European journal of operational research 230,

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problem with simultaneous pickup and delivery, International journal of engineering

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optimization algorithm for multi-depot vehicle routing problem with pickup and

delivery requests, International conference of engineers and computer scientists,

Vol. III, March 17-19, 2010, Hong Kong.

48. Ismail Karaoglan, Fulya Altiparmak, Imdat Kara, Berna Dengiz (2011) A branch and

cut algorithm for the location-routing problem with simultaneous pickup and

delivery, European journal of operational research 211 (2011), pp. 318–332.

49. Jafari-Eskandari M., Sadjadi SJ., Jabalameli MS., Bozorgi-Amiri A. (2009) A robust

optimization approach for the milk run problem (An auto industry supply chain case

study). International conference on computers & industrial engineering, 6–9 July

2009, Troyes, France, pp. 1076–1081.

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consolidating milk-runs, Transportation research , Vol. 43, No. 5, pp. 565–577,

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used in MANETs routing, International journal of computer applications technology

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162, pp. 126–141.

102

List of Publication

1. Diptesh Patel and Dr. M.B.Patel (2013): Optimization approach of vehicle routing by a

milk-run material supply system, International Journal for Scientific Research &

Development, ISSN: 2321-0613, Volume 1, Issue 6, July 2013, pp: 1315-1318.

2. Diptesh Patel and Dr. M.B.Patel (2013): Design and development of an internal milk-

run material supply system in automotive industry, International Journal of Application

or Innovation in Engineering & Management, ISSN: 2319 – 4847, Volume 2, Issue 8,

August 2013, pp: 233-235.

3. Diptesh Patel, Dr. M.B.Patel and Dr. J.A. Vadher (2014): Implementation of milk run

material supply system in vehicle routing problem with simultaneous pickup and

delivery, International Journal of Application or Innovation in Engineering &

Management, ISSN: 2319 – 4847, Volume 3, Issue 11, November 2014, pp: 122-124.

4. Diptesh Patel, Dr. M.B.Patel and Dr. J.A. Vadher (2017): Design and development of

milk-run material supply system with time windows and simultaneous pickups and

deliveries, International Journal for Innovative Research in Science & Technology,

ISSN : 2349-6010, Volume 4, Issue 2, July 2017 : 156-166.

103

APPENDIX – 1

Graphical User Interface

function varargout = All_in_one(varargin)

% ALL_IN_ONE MATLAB code for All_in_one.fig

% ALL_IN_ONE, by itself, creates a new ALL_IN_ONE or raises the existing

% singleton*.

%

% H = ALL_IN_ONE returns the handle to a new ALL_IN_ONE or the handle to

% the existing singleton*.

%

% ALL_IN_ONE('CALLBACK',hObject,eventData,handles,...) calls the local

% function named CALLBACK in ALL_IN_ONE.M with the given input arguments.

%

% ALL_IN_ONE('Property','Value',...) creates a new ALL_IN_ONE or raises the

% existing singleton*. Starting from the left, property value pairs are

% applied to the GUI before All_in_one_OpeningFcn gets called. An

% unrecognized property name or invalid value makes property application

% stop. All inputs are passed to All_in_one_OpeningFcn via varargin.

%

% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one

% instance to run (singleton)".

%

% See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help All_in_one

% Last Modified by GUIDE v2.5 03-Oct-2016 16:11:16

% Begin initialization code - DO NOT EDIT

gui_Singleton = 1;

gui_State = struct('gui_Name', mfilename, ...

'gui_Singleton', gui_Singleton, ...

'gui_OpeningFcn', @All_in_one_OpeningFcn, ...

104

'gui_OutputFcn', @All_in_one_OutputFcn, ...

'gui_LayoutFcn', [] , ...

'gui_Callback', []);

if nargin && ischar(varargin{1})

gui_State.gui_Callback = str2func(varargin{1});

end

if nargout

[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});

else

gui_mainfcn(gui_State, varargin{:});

end

% End initialization code - DO NOT EDIT

% --- Executes just before All_in_one is made visible.

function All_in_one_OpeningFcn(hObject, eventdata, handles, varargin)

% This function has no output args, see OutputFcn.

% hObject handle to figure

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% varargin command line arguments to All_in_one (see VARARGIN)

% Choose default command line output for All_in_one

handles.output = hObject;

%create an axes that spans the whole gui

ah = axes('unit', 'normalized', 'position', [0 0 1 1]);

% import the background image and show it on the axes

bg = imread('images.jpg'); imagesc(bg);

% prevent plotting over the background and turn the axis off

set(ah,'handlevisibility','off','visible','off')

% making sure the background is behind all the other uicontrols

uistack(ah, 'bottom');

105

% Update handles structure

guidata(hObject, handles);

% UIWAIT makes All_in_one wait for user response (see UIRESUME)

% uiwait(handles.figure1);

% --- Outputs from this function are returned to the command line.

function varargout = All_in_one_OutputFcn(hObject, eventdata, handles)

% varargout cell array for returning output args (see VARARGOUT);




% Get default command line output from handles structure

varargout{1} = handles.output;

% --- Executes on button press in Schedule.

function Schedule_Callback(hObject, eventdata, handles)

% hObject handle to Schedule (see GCBO)



run untitled1.m

% --- Executes on button press in WD.

function WD_Callback(hObject, eventdata, handles)

% hObject handle to WD (see GCBO)



run untitled.m

% --- Executes on button press in VR.

function VR_Callback(hObject, eventdata, handles)

% hObject handle to VR (see GCBO)


106


run Vad.m

% --- Executes on button press in AR.

function AR_Callback(hObject, eventdata, handles)

% hObject handle to AR (see GCBO)



run Ahm.m

% --- Executes on button press in MR.

function MR_Callback(hObject, eventdata, handles)

% hObject handle to MR (see GCBO)



run Meh.m

% --- Executes on button press in RR.

function RR_Callback(hObject, eventdata, handles)

% hObject handle to RR (see GCBO)



run Raj.m

% --- Executes on button press in GA.

function GA_Callback(hObject, eventdata, handles)

% hObject handle to GA (see GCBO)



run mtspf_ga_GOOOD_ONE.m

% --- Executes on button press in S2S.

function S2S_Callback(hObject, eventdata, handles)

% hObject handle to S2S (see GCBO)



run XPERIMENT.m

107

% --- Executes on button press in SR.

function SR_Callback(hObject, eventdata, handles)

% hObject handle to SR (see GCBO)



run Sur.m

% --- Executes on button press in Close.

function Close_Callback(hObject, eventdata, handles)

% hObject handle to Close (see GCBO)



close All_in_one.fig

% --- Executes on button press in GR.

function GR_Callback(hObject, eventdata, handles)

% hObject handle to GR (see GCBO)



run Gujarat.m

% --- Executes on button press in co2.

function co2_Callback(hObject, eventdata, handles)

% hObject handle to co2 (see GCBO)



run CO2_EMISSION.m

Published with MATLAB® R2015a

http://www.mathworks.com/products/matlab

108

APPENDIX – 2

Database of Destination City

function [w22,w0,w01]=city_name_dt(dt_in)

[x,ahm]=xlsread('city_name.xlsx','ahm')

[x,meh]=xlsread('city_name.xlsx','meh')

[x,raj]=xlsread('city_name.xlsx','raj')

[x,sur]=xlsread('city_name.xlsx','sur')

[x,vad]=xlsread('city_name.xlsx','vad')

X1=strcmpi(ahm,dt_in)

X01=strcmpi(meh,dt_in)

X02=strcmpi(raj,dt_in)

X03=strcmpi(sur,dt_in)

X04=strcmpi(vad,dt_in)

if sum(sum(X1))==1

[w0,st_loc]=find(X1==1)

if st_loc==1

w01=num2str(st_loc)

w22=strcat('Ahm_',w01)

elseif st_loc==2

w01=num2str(st_loc)


elseif st_loc==3

w01=num2str(st_loc)


else

109

w01=num2str(st_loc)


end

%11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111

11111111%

elseif sum(sum(X01))==1


if st_loc==1

w01=num2str(st_loc)

w22=strcat('Meh_',w01)

elseif st_loc==2

w01=num2str(st_loc)


elseif st_loc==3

w01=num2str(st_loc)


else

w01=num2str(st_loc)


end

%22222222222222222222222222222222222222222222222222222222222222222222222222222222222222222

2222222222222%



if st_loc==1

w01=num2str(st_loc)

w22=strcat('Raj_',w01)

elseif st_loc==2

w01=num2str(st_loc)


elseif st_loc==3

w01=num2str(st_loc)


else

w01=num2str(st_loc)

110


end

%33333333333333333333333333333333333333333333333333333333333333333333333333333333333333333

33333333333%



if st_loc==1

w01=num2str(st_loc)

w22=strcat('Sur_',w01)

elseif st_loc==2

w01=num2str(st_loc)


elseif st_loc==3

w01=num2str(st_loc)


else

w01=num2str(st_loc)


end

%44444444444444444444444444444444444444444444444444444444444444444444444444444444444444444

444444%



if st_loc==1

w01=num2str(st_loc)

w22=strcat('Vad_',w01)

elseif st_loc==2

w01=num2str(st_loc)


elseif st_loc==3

w01=num2str(st_loc)


else

w01=num2str(st_loc)


end

111

else disp('Enter the valid city name')

end

%55555555555555555555555555555555555555555555555555555555555555555555555555555555555555555

5555555%

end



112

APPENDIX – 3

Database of Starting City

function [w002,w0,w01]=city_name_st(st_in)

major={'Ahmedabad','Mehsana','Vadodara','Surat','Rajkot'}

[x,ALL]=xlsread('city_name.xlsx','All')

[x,ahm]=xlsread('city_name.xlsx','ahm')

[x,meh]=xlsread('city_name.xlsx','meh')

[x,raj]=xlsread('city_name.xlsx','raj')

[x,sur]=xlsread('city_name.xlsx','sur')

[x,vad]=xlsread('city_name.xlsx','vad')

X0=strcmpi(ALL,st_in)

X1=strcmpi(ahm,st_in)

X01=strcmpi(meh,st_in)

X02=strcmpi(raj,st_in)

X03=strcmpi(sur,st_in)

X04=strcmpi(vad,st_in)

if sum(sum(X0))==1

[st_loc,w0]=find(X0==1)

if st_loc==1

w01=num2str(st_loc)

w002='Rajkot'

elseif st_loc==2

w01=num2str(st_loc)

w002='Ahmedabad'

elseif st_loc==3

w01=num2str(st_loc)

113

w002='Vadodara'

elseif st_loc==4

w01=num2str(st_loc)

w002='Surat'

elseif st_loc==5

w01=num2str(st_loc)

w002='Mehsana'

end % ALL are considered ...................................................%



if st_loc==1

w01=num2str(st_loc)


elseif st_loc==2

w01=num2str(st_loc)


elseif st_loc==3

w01=num2str(st_loc)


else

w01=num2str(st_loc)


end

%11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111

11111111%



if st_loc==1

w01=num2str(st_loc)


elseif st_loc==2

w01=num2str(st_loc)


elseif st_loc==3

w01=num2str(st_loc)

114


else

w01=num2str(st_loc)


end

%22222222222222222222222222222222222222222222222222222222222222222222222222222222222222222

2222222222222%



if st_loc==1

w01=num2str(st_loc)


elseif st_loc==2

w01=num2str(st_loc)


elseif st_loc==3

w01=num2str(st_loc)


else

w01=num2str(st_loc)


end

%33333333333333333333333333333333333333333333333333333333333333333333333333333333333333333

33333333333%



if st_loc==1

w01=num2str(st_loc)


elseif st_loc==2

w01=num2str(st_loc)


elseif st_loc==3

w01=num2str(st_loc)


115

else

w01=num2str(st_loc)


end

%44444444444444444444444444444444444444444444444444444444444444444444444444444444444444444

444444%



if st_loc==1

w01=num2str(st_loc)


elseif st_loc==2

w01=num2str(st_loc)


elseif st_loc==3

w01=num2str(st_loc)


else

w01=num2str(st_loc)


end

else disp('Enter the Valid city name')

%55555555555555555555555555555555555555555555555555555555555555555555555555555555555555555

5555555%

end

end



116

APPENDIX – 4

Calculation of CO2 Gas Emission

function varargout = CO2_EMISSION(varargin)

% CO2_EMISSION MATLAB code for CO2_EMISSION.fig

% CO2_EMISSION, by itself, creates a new CO2_EMISSION or raises the existing

% singleton*.

%

% H = CO2_EMISSION returns the handle to a new CO2_EMISSION or the handle to


%

% CO2_EMISSION('CALLBACK',hObject,eventData,handles,...) calls the local

% function named CALLBACK in CO2_EMISSION.M with the given input arguments.

%

% CO2_EMISSION('Property','Value',...) creates a new CO2_EMISSION or raises the


% applied to the GUI before CO2_EMISSION_OpeningFcn gets called. An


% stop. All inputs are passed to CO2_EMISSION_OpeningFcn via varargin.

%



%


% Edit the above text to modify the response to help CO2_EMISSION



gui_Singleton = 1;



'gui_OpeningFcn', @CO2_EMISSION_OpeningFcn, ...

117

'gui_OutputFcn', @CO2_EMISSION_OutputFcn, ...





end

if nargout


else


end


% --- Executes just before CO2_EMISSION is made visible.

function CO2_EMISSION_OpeningFcn(hObject, eventdata, handles, varargin)





% varargin command line arguments to CO2_EMISSION (see VARARGIN)

% Choose default command line output for CO2_EMISSION










118



% UIWAIT makes CO2_EMISSION wait for user response (see UIRESUME)



function varargout = CO2_EMISSION_OutputFcn(hObject, eventdata, handles)







function NV_Callback(hObject, eventdata, handles)

% hObject handle to NV (see GCBO)



input=(get(hObject,'String'))

% Hints: get(hObject,'String') returns contents of NV as text

% str2double(get(hObject,'String')) returns contents of NV as a double

% --- Executes during object creation, after setting all properties.

function NV_CreateFcn(hObject, eventdata, handles)

% hObject handle to NV (see GCBO)


% handles empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))

119

set(hObject,'BackgroundColor','white');

end

function MV_Callback(hObject, eventdata, handles)

% hObject handle to MV (see GCBO)




% Hints: get(hObject,'String') returns contents of MV as text

% str2double(get(hObject,'String')) returns contents of MV as a double


function MV_CreateFcn(hObject, eventdata, handles)

% hObject handle to MV (see GCBO)







end

function DT_Callback(hObject, eventdata, handles)

% hObject handle to DT (see GCBO)




% Hints: get(hObject,'String') returns contents of DT as text

% str2double(get(hObject,'String')) returns contents of DT as a double

120


function DT_CreateFcn(hObject, eventdata, handles)

% hObject handle to DT (see GCBO)







end

% --- Executes on button press in pushbutton1.

function pushbutton1_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton1 (see GCBO)



EFCO2=0.515

MV=(get(handles.MV,'string'))

NV=(get(handles.NV,'string'))

DT=(get(handles.DT,'string'))

NV1= str2double(NV)

DT1= str2double(DT)

MV1= str2double(MV)

X=1/MV1

CO2EM=NV1*(DT1+100)*X*EFCO2

set(handles.text5,'String',CO2EM)



121

APPENDIX – 5

Coding of Milk-Run Routing Supply System

st=input('Enter the name of starting city:','s')

dt=input('Enter the name of Destination city:','s')

Vehicle=input('Enter the number of Vehicles:')


major01=xlsread('city_name.xlsx','All')

major02=xlsread('city_name.xlsx','All_1')


[Major_Name,Major_kms]=xlsread('Major kms.xlsx')

[ans,ans01,ans02]=city_name_st(st)

st_cl=ans01

st_rw=str2num(ans02)

st_name=ans

[ans,ans01,ans02]=city_name_dt(dt)

dt_cl=ans01

dt_rw=str2num(ans02)

dt_name=ans

% Startinf route Data ...................................................%

st01=st_name(1:3)

sheetnoS=st_name(5)

[x,y]=xlsread('city_name.xlsx',st01)

st_row=y(:,st_rw)

st_route=st_row(st_cl:length(st_row))

% Destination route data..................................................%

dt01=dt_name(1:3)

122

sheetnoD=dt_name(5)

[x1,y1]=xlsread('city_name.xlsx',dt01)

dt_row=y1(:,dt_rw)

dt_route=dt_row(1:dt_cl)

if strcmpi(dt01,'ahm')==1

MAJOR_dt=major(1)

elseif strcmpi(dt01,'meh')==1

MAJOR_dt=major(2)

elseif strcmpi(dt01,'vad')==1

MAJOR_dt=major(3)

elseif strcmpi(dt01,'sur')==1

MAJOR_dt=major(4)

elseif strcmpi(dt01,'raj')==1

MAJOR_dt=major(5)

end

% ............MAJOR DESTINATION .......................................................%

if strcmpi(st01,'ahm')==1

MAJOR_st=major(1)

elseif strcmpi(st01,'meh')==1

MAJOR_st=major(2)

elseif strcmpi(st01,'vad')==1

MAJOR_st=major(3)

elseif strcmpi(st01,'sur')==1

MAJOR_st=major(4)

elseif strcmpi(st01,'raj')==1

MAJOR_st=major(5)

end

% .... MAJOR CITY ROUTING DATA EXCEL SHEET.........................................................%

Mejor_city=strcat(st01,'_',dt01)

MJR_XL=xlsread('Regional.xlsx',Mejor_city)

route_sheet=xlsread('ROUTE SHEET.xlsx',st_name)

st_num_S=num2str(route_sheet(st_cl,2))

L0=length(route_sheet)

st_num_E=num2str(route_sheet(L0,2))

123

RANGE_S=num2str(strcat('A',st_num_S,':','B',st_num_E))

ST_XL=xlsread('Routing_data.xlsx',st_name,RANGE_S)

%___________________________________________________________________________________%

route_sheet=xlsread('ROUTE SHEET.xlsx',dt_name)

dt_num_D1=num2str(route_sheet(dt_cl,2))

RANGE_D=num2str(strcat('A1',':','B',dt_num_D1))

DT_XL=xlsread('Routing_data.xlsx',dt_name,RANGE_D)

Origin_st_data=[ST_XL;MJR_XL;DT_XL]

% ............................................................%

plot(Origin_st_data(:, 1), Origin_st_data(:, 2),'r','Marker','.', 'LineWidth', 3.5);

line(Origin_st_data(1, 1), Origin_st_data(1, 2), 'Marker', 's', ...

'Color', 'b', 'MarkerFaceColor', 'b', 'MarkerSize', 10);

line(Origin_st_data(end, 1), Origin_st_data(end, 2), 'Marker', 's', ...


xlim([69.50,74.30]);

% Google map%

plot_google_map('maptype', 'roadmap');

[Major_Kms,Major_Name]=xlsread('Major kms.xlsx')

if strcmpi(MAJOR_st,Major_Name(1))==1

Q=1

elseif strcmpi(MAJOR_st,Major_Name(2))==1

Q=2


Q=3


Q=4


Q=5

end

124

if strcmpi(MAJOR_dt,Major_Name(1))==1

O=1

elseif strcmpi(MAJOR_dt,Major_Name(2))==1

O=2


O=3


O=4


O=5

end

M_KMS=Major_Kms(Q,O)

St_range_cost=xlsread('All Routes kms.xlsx',st_name)

st_KMS=St_range_cost(st_cl,3)

Dt_range_cost=xlsread('All Routes kms.xlsx',dt_name)

dt_KMS=Dt_range_cost(dt_cl,2)

Total_KMS=st_KMS+dt_KMS+M_KMS

Vehicle_cost=Vehicle*330

Travelling_cost=Total_KMS*8

T_K=num2str(Total_KMS)

SUB_TIME=Total_KMS/50

TOTAL_HOUR=fix(SUB_TIME)

TOTAL_HOUR1=num2str(TOTAL_HOUR)

TOTAL_MIN=num2str(round((SUB_TIME*60)-(TOTAL_HOUR*60),0))

Total_cost=Vehicle_cost+Travelling_cost

Veh=num2str(Vehicle)

T_C=num2str(Total_cost)

DIPS1=strcat('For Traveling of : ',T_K,'','Kms','with',Veh,'of vehicals','.The Expected time for Journey

is:',TOTAL_HOUR1,' HOURS and','',TOTAL_MIN,' minutes','and total cost for traveling is',T_C)

125

APPENDIX – 6

Multiple Travelling Salesman Problem with Genetic

Algorithm

% MTSPF_GA Fixed Multiple Traveling Salesmen Problem (M-TSP) Genetic Algorithm (GA)

% Finds a (near) optimal solution to a variation of the M-TSP by setting

% up a GA to search for the shortest route (least distance needed for

% each salesman to travel from the start location to individual cities

% and back to the original starting place)

%

% Summary:

% 1. Each salesman starts at the first point, and ends at the first

% point, but travels to a unique set of cities in between

% 2. Except for the first, each city is visited by exactly one salesman

%

% Note: The Fixed Start/End location is taken to be the first XY point

%

% Input:

% USERCONFIG (structure) with zero or more of the following fields:

% - XY (float) is an Nx2 matrix of city locations, where N is the number of cities

% - DMAT (float) is an NxN matrix of city-to-city distances or costs

% - NSALESMEN (scalar integer) is the number of salesmen to visit the cities

% - MINTOUR (scalar integer) is the minimum tour length for any of the

% salesmen, NOT including the start/end point

% - POPSIZE (scalar integer) is the size of the population (should be divisible by 8)

% - NUMITER (scalar integer) is the number of desired iterations for the algorithm to run

% - SHOWPROG (scalar logical) shows the GA progress if true

% - SHOWRESULT (scalar logical) shows the GA results if true

% - SHOWWAITBAR (scalar logical) shows a waitbar if true

%

% Input Notes:

% 1. Rather than passing in a structure containing these fields, any/all of

% these inputs can be passed in as parameter/value pairs in any order instead.

126

% 2. Field/parameter names are case insensitive but must match exactly otherwise.

%

% Output:

% RESULTSTRUCT (structure) with the following fields:

% (in addition to a record of the algorithm configuration)

% - OPTROUTE (integer array) is the best route found by the algorithm

% - OPTBREAK (integer array) is the list of route break points (these specify the indices

% into the route used to obtain the individual salesman routes)

% - MINDIST (scalar float) is the total distance traveled by the salesmen

%

% Route/Breakpoint Details:

% If there are 10 cities and 3 salesmen, a possible route/break

% combination might be: rte = [5 6 9 4 2 8 10 3 7], brks = [3 7]

% Taken together, these represent the solution [1 5 6 9 1][1 4 2 8 10 1][1 3 7 1],

% which designates the routes for the 3 salesmen as follows:

% . Salesman 1 travels from city 1 to 5 to 6 to 9 and back to 1

% . Salesman 2 travels from city 1 to 4 to 2 to 8 to 10 and back to 1

% . Salesman 3 travels from city 1 to 3 to 7 and back to 1

%

% Usage:

% mtspf_ga

% -or-

% mtspf_ga(userConfig)

% -or-

% resultStruct = mtspf_ga;

% -or-

% resultStruct = mtspf_ga(userConfig);

% -or-

% [...] = mtspf_ga('Param1',Value1,'Param2',Value2, ...);

%

% Example:

% % Let the function create an example problem to solve

% mtspf_ga;

%

% Example:

% % Request the output structure from the solver

% resultStruct = mtspf_ga;

127

%

% Example:

% % Pass a random set of user-defined XY points to the solver

% userConfig = struct('xy',10*rand(35,2));


%

% Example:

% % Pass a more interesting set of XY points to the solver

% n = 50;

% phi = (sqrt(5)-1)/2;

% theta = 2*pi*phi*(0:n-1);

% rho = (1:n).^phi;

% [x,y] = pol2cart(theta(:),rho(:));

% xy = 10*([x y]-min([x;y]))/(max([x;y])-min([x;y]));

% userConfig = struct('xy',xy);


%

% Example:

% % Pass a random set of 3D (XYZ) points to the solver

% xyz = 10*rand(35,3);

% userConfig = struct('xy',xyz);


%

% Example:

% % Change the defaults for GA population size and number of iterations

% userConfig = struct('popSize',200,'numIter',1e4);


%

% Example:

% % Turn off the plots but show a waitbar

% userConfig = struct('showProg',false,'showResult',false,'showWaitbar',true);


%

% See also: mtsp_ga, mtspo_ga, mtspof_ga, mtspofs_ga, mtspv_ga, distmat

%

% Author: Joseph Kirk

% Email: [email protected]

128

% Release: 2.0

% Release Date: 05/01/2014

function varargout = mtspf_ga(varargin)

% Initialize default configuration

defaultConfig.xy = xlsread('METRIX_All.xlsx','Vado_1');

defaultConfig.dmat = xlsread('METRIX_All.xlsx','Vado_2');

defaultConfig.nSalesmen = 2;

defaultConfig.minTour = 5;

defaultConfig.popSize = 80;

defaultConfig.numIter = 5e3;

defaultConfig.showProg = true;

defaultConfig.showResult = true;

defaultConfig.showWaitbar = false;

% Interpret user configuration inputs

if ~nargin

userConfig = struct();

elseif isstruct(varargin{1})

userConfig = varargin{1};

else

try

userConfig = struct(varargin{:});

catch

error('Expected inputs are either a structure or parameter/value pairs');

end

end

% Override default configuration with user inputs

configStruct = get_config(defaultConfig,userConfig);

% Extract configuration

xy = configStruct.xy;

dmat = configStruct.dmat;

nSalesmen = configStruct.nSalesmen;

minTour = configStruct.minTour;

popSize = configStruct.popSize;

129

numIter = configStruct.numIter;

showProg = configStruct.showProg;

showResult = configStruct.showResult;

showWaitbar = configStruct.showWaitbar;

if isempty(dmat)

nPoints = size(xy,1);

a = meshgrid(1:nPoints);

dmat = reshape(sqrt(sum((xy(a,:)-xy(a',:)).^2,2)),nPoints,nPoints);

end

% Verify Inputs

[N,dims] = size(xy);

[nr,nc] = size(dmat);

if N ~= nr || N ~= nc

error('Invalid XY or DMAT inputs!')

end

n = N - 1; % Separate Start/End City

% Sanity Checks

nSalesmen = max(1,min(n,round(real(nSalesmen(1)))));

minTour = max(1,min(floor(n/nSalesmen),round(real(minTour(1)))));

popSize = max(8,8*ceil(popSize(1)/8));

numIter = max(1,round(real(numIter(1))));

showProg = logical(showProg(1));

showResult = logical(showResult(1));

showWaitbar = logical(showWaitbar(1));

% Initializations for Route Break Point Selection

nBreaks = nSalesmen-1;

dof = n - minTour*nSalesmen; % degrees of freedom

addto = ones(1,dof+1);

for k = 2:nBreaks

addto = cumsum(addto);

end

cumProb = cumsum(addto)/sum(addto);

% Initialize the Populations

130

popRoute = zeros(popSize,n); % population of routes

popBreak = zeros(popSize,nBreaks); % population of breaks

popRoute(1,:) = (1:n) + 1;

popBreak(1,:) = rand_breaks();

for k = 2:popSize

popRoute(k,:) = randperm(n) + 1;

popBreak(k,:) = rand_breaks();

end

% Select the Colors for the Plotted Routes

pclr = ~get(0,'DefaultAxesColor');

clr = [1 0 0; 0 0 1; 0.67 0 1; 0 1 0; 1 0.5 0];

if nSalesmen > 5

clr = hsv(nSalesmen);

end

% Run the GA

globalMin = Inf;

totalDist = zeros(1,popSize);

distHistory = zeros(1,numIter);

tmpPopRoute = zeros(8,n);

tmpPopBreak = zeros(8,nBreaks);

newPopRoute = zeros(popSize,n);

newPopBreak = zeros(popSize,nBreaks);

if showProg

figure('Name','MTSPF_GA | Current Best Solution','Numbertitle','off');

hAx = gca;

end

if showWaitbar

hWait = waitbar(0,'Searching for near-optimal solution ...');

end

for iter = 1:numIter

% Evaluate Members of the Population

for p = 1:popSize

d = 0;

pRoute = popRoute(p,:);

pBreak = popBreak(p,:);

131

rng = [[1 pBreak+1];[pBreak n]]';

for s = 1:nSalesmen

d = d + dmat(1,pRoute(rng(s,1))); % Add Start Distance

for k = rng(s,1):rng(s,2)-1

d = d + dmat(pRoute(k),pRoute(k+1));

end

d = d + dmat(pRoute(rng(s,2)),1); % Add End Distance

end

totalDist(p) = d;

end

% Find the Best Route in the Population

[minDist,index] = min(totalDist);

distHistory(iter) = minDist;

if minDist < globalMin

globalMin = minDist;

optRoute = popRoute(index,:);

optBreak = popBreak(index,:);

rng = [[1 optBreak+1];[optBreak n]]';

if showProg

% Plot the Best Route

for s = 1:nSalesmen

rte = [1 optRoute(rng(s,1):rng(s,2)) 1];

if dims > 2, plot3(hAx,xy(rte,1),xy(rte,2),xy(rte,3),'.-','Color',clr(s,:));

else plot(hAx,xy(rte,1),xy(rte,2),'.-','Color',clr(s,:)); end

hold(hAx,'on');

end

if dims > 2, plot3(hAx,xy(1,1),xy(1,2),xy(1,3),'o','Color',pclr);

else plot(hAx,xy(1,1),xy(1,2),'o','Color',pclr); end

title(hAx,sprintf('Total Distance = %1.4f, Iteration = %d',minDist,iter));

hold(hAx,'off');

drawnow;

end

end

% Genetic Algorithm Operators

randomOrder = randperm(popSize);

132

for p = 8:8:popSize

rtes = popRoute(randomOrder(p-7:p),:);

brks = popBreak(randomOrder(p-7:p),:);

dists = totalDist(randomOrder(p-7:p));

[ignore,idx] = min(dists); %#ok

bestOf8Route = rtes(idx,:);

bestOf8Break = brks(idx,:);

routeInsertionPoints = sort(ceil(n*rand(1,2)));

I = routeInsertionPoints(1);

J = routeInsertionPoints(2);

for k = 1:8 % Generate New Solutions

tmpPopRoute(k,:) = bestOf8Route;

tmpPopBreak(k,:) = bestOf8Break;

switch k

case 2 % Flip

tmpPopRoute(k,I:J) = tmpPopRoute(k,J:-1:I);

case 3 % Swap

tmpPopRoute(k,[I J]) = tmpPopRoute(k,[J I]);

case 4 % Slide

tmpPopRoute(k,I:J) = tmpPopRoute(k,[I+1:J I]);

case 5 % Modify Breaks

tmpPopBreak(k,:) = rand_breaks();

case 6 % Flip, Modify Breaks

tmpPopRoute(k,I:J) = tmpPopRoute(k,J:-1:I);


case 7 % Swap, Modify Breaks

tmpPopRoute(k,[I J]) = tmpPopRoute(k,[J I]);


case 8 % Slide, Modify Breaks

tmpPopRoute(k,I:J) = tmpPopRoute(k,[I+1:J I]);


otherwise % Do Nothing

end

end

newPopRoute(p-7:p,:) = tmpPopRoute;

newPopBreak(p-7:p,:) = tmpPopBreak;

end

133

popRoute = newPopRoute;

popBreak = newPopBreak;

% Update the waitbar

if showWaitbar && ~mod(iter,ceil(numIter/325))

waitbar(iter/numIter,hWait);

end

end

if showWaitbar

close(hWait);

end

if showResult

% Plots

figure('Name','MTSPF_GA | Results','Numbertitle','off');

subplot(2,2,1);

if dims > 2, plot3(xy(:,1),xy(:,2),xy(:,3),'.','Color',pclr);

else plot(xy(:,1),xy(:,2),'.','Color',pclr); end

title('City Locations');

subplot(2,2,2);

imagesc(dmat([1 optRoute],[1 optRoute]));

title('Distance Matrix');

subplot(2,2,3);

rng = [[1 optBreak+1];[optBreak n]]';

for s = 1:nSalesmen

rte = [1 optRoute(rng(s,1):rng(s,2)) 1];

if dims > 2, plot3(xy(rte,1),xy(rte,2),xy(rte,3),'.-','Color',clr(s,:));

else plot(xy(rte,1),xy(rte,2),'.-','Color',clr(s,:)); end

title(sprintf('Total Distance = %1.4f',minDist));

hold on;

end

if dims > 2, plot3(xy(1,1),xy(1,2),xy(1,3),'o','Color',pclr);

else plot(xy(1,1),xy(1,2),'o','Color',pclr); end

subplot(2,2,4);

plot(distHistory,'b','LineWidth',2);

title('Best Solution History');

134

set(gca,'XLim',[0 numIter+1],'YLim',[0 1.1*max([1 distHistory])]);

end

% Return Output

if nargout

resultStruct = struct( ...

'xy', xy, ...

'dmat', dmat, ...

'nSalesmen', nSalesmen, ...

'minTour', minTour, ...

'popSize', popSize, ...

'numIter', numIter, ...

'showProg', showProg, ...

'showResult', showResult, ...

'showWaitbar', showWaitbar, ...

'optRoute', optRoute, ...

'optBreak', optBreak, ...

'minDist', minDist);

varargout = {resultStruct};

end

% Generate Random Set of Break Points

function breaks = rand_breaks()

if minTour == 1 % No Constraints on Breaks

tmpBreaks = randperm(n-1);

breaks = sort(tmpBreaks(1:nBreaks));

else % Force Breaks to be at Least the Minimum Tour Length

nAdjust = find(rand < cumProb,1)-1;

spaces = ceil(nBreaks*rand(1,nAdjust));

adjust = zeros(1,nBreaks);

for kk = 1:nBreaks

adjust(kk) = sum(spaces == kk);

end

breaks = minTour*(1:nBreaks) + cumsum(adjust);

end

end

135

end

% Subfunction to override the default configuration with user inputs

function config = get_config(defaultConfig,userConfig)

% Initialize the configuration structure as the default

config = defaultConfig;

% Extract the field names of the default configuration structure

defaultFields = fieldnames(defaultConfig);

% Extract the field names of the user configuration structure

userFields = fieldnames(userConfig);

nUserFields = length(userFields);

% Override any default configuration fields with user values

for i = 1:nUserFields

userField = userFields{i};

isField = strcmpi(defaultFields,userField);

if nnz(isField) == 1

thisField = defaultFields{isField};

config.(thisField) = userConfig.(userField);

end

end

end



136

APPENDIX – 7

Milk-Run Routes with Weight Distribution

function varargout = untitled(varargin)

% UNTITLED MATLAB code for untitled.fig

% UNTITLED, by itself, creates a new UNTITLED or raises the existing

% singleton*.

%

% H = UNTITLED returns the handle to a new UNTITLED or the handle to


%

% UNTITLED('CALLBACK',hObject,eventData,handles,...) calls the local

% function named CALLBACK in UNTITLED.M with the given input arguments.

%

% UNTITLED('Property','Value',...) creates a new UNTITLED or raises the


% applied to the GUI before untitled_OpeningFcn gets called. An


% stop. All inputs are passed to untitled_OpeningFcn via varargin.

%



%


% Edit the above text to modify the response to help untitled

% Last Modified by GUIDE v2.5 22-Sep-2016 11:00:51


gui_Singleton = 1;



'gui_OpeningFcn', @untitled_OpeningFcn, ...

137

'gui_OutputFcn', @untitled_OutputFcn, ...





end

if nargout


else


end


% --- Executes just before untitled is made visible.

function untitled_OpeningFcn(hObject, eventdata, handles, varargin)





% varargin command line arguments to untitled (see VARARGIN)

% Choose default command line output for untitled










138



% UIWAIT makes untitled wait for user response (see UIRESUME)



function varargout = untitled_OutputFcn(hObject, eventdata, handles)







% --- Executes on button press in SIMU.

function SIMU_Callback(hObject, eventdata, handles)

% hObject handle to SIMU (see GCBO)



Data006=xlsread('E01.xlsx','All')

Data007=xlsread('E01.xlsx','All_1')

axes(handles.MAP)

% plot route data

plot(Data006(:, 1), Data006(:, 2),'color',rgb('Crimson'), 'LineWidth', 2);

hold on

% plot route data


xlim([69.50,74.30]);

ylim([20.4,24.6]);

% Google map


139

%Plotting coloures .................................................%

Red='r'

Magenta='m'

Cyan='c'

Green='g'

C01=rgb('Green')

C02=rgb('OrangeRed')

C03=rgb('Salmon')

C04=rgb('DarkRed')

%......................... ROUTE SHEET ...............................%

ahm01=xlsread('ROUTE SHEET.xlsx','Ahm_1')



meh01=xlsread('ROUTE SHEET.xlsx','Meh_1')


raj01=xlsread('ROUTE SHEET.xlsx','Raj_1')




sur01=xlsread('ROUTE SHEET.xlsx','Sur_1')


vad01=xlsread('ROUTE SHEET.xlsx','Vad_1')


% ......................... ACTUAL ROUTING DATA............... %

Ahm011=xlsread('Routing_data.xlsx','Ahm_1')



Meh011=xlsread('Routing_data.xlsx','Meh_1')


Raj011=xlsread('Routing_data.xlsx','Raj_1')




Sur011=xlsread('Routing_data.xlsx','Sur_1')


Vad011=xlsread('Routing_data.xlsx','Vad_1')

140


% ................... CODING........................................%

Xpres={'Ahm011','Ahm021','Ahm031','Meh011','Meh021','Raj011','Raj021','Raj031','Raj041','Sur011','Sur021','V

ad011','Vad021'}

for k=1:length(Xpres)

hold on

O=Xpres(k)

O1=char(O)

w01=O1(1:3)

w02=O1(5)

X=strcat(w01,'_',w02)

Universal_data=xlsread('Routing_data.xlsx',X)

plot(Universal_data(:, 1), Universal_data(:, 2),'color',rgb('Crimson'),'Marker','.', 'LineWidth', 3.5);

xlim([69.50,74.30]);

ylim([20.4,24.6]);

end

hold on

for i=1:length(Xpres)

hold on

O=Xpres(i)

O1=char(O)

w01=O1(1:3)

w02=O1(5)



[RS,RC]=xlsread('ROUTE SHEET.xlsx',X)

[r,c]=size(RS)

for j=1:(r-1)

Weight=RS(j,2)

X1=num2str(RS(j,2))

Y=num2str(RS(j+1,2))

Range=strcat('A',X1,':','B',Y)

Origin_st_data=xlsread('Routing_data.xlsx',X,Range)

141

START_CITY=RC(j,1)

DESTI_CITY=RC(j+1,1)

if Weight<175

plot(Origin_st_data(:, 1), Origin_st_data(:, 2),'color',rgb('LightGreen'),'Marker','.', 'LineWidth', 3.5);

xlim([69.50,74.30]);

ylim([20.4,24.6]);

% Google map


elseif Weight>175 & Weight<350

plot(Origin_st_data(:, 1), Origin_st_data(:, 2),'color',rgb('Yellow'),'Marker','.', 'LineWidth', 3.5);

xlim([69.50,74.30]);

ylim([20.4,24.6]);

% Google map



plot(Origin_st_data(:, 1), Origin_st_data(:, 2),'color',rgb('Orange'),'Marker','.', 'LineWidth', 3.5);

xlim([69.50,74.30]);

ylim([20.4,24.6]);

% Google map


elseif Weight>700

plot(Origin_st_data(:, 1), Origin_st_data(:, 2),'color',rgb('Crimson'),'Marker','.', 'LineWidth', 3.5);

xlim([69.50,74.30]);

ylim([20.4,24.6]);

142

% Google map


end

set(handles.ST_CT,'string',START_CITY)

set(handles.DT_CT,'string',DESTI_CITY)

set(handles.WT,'string',Weight)

end

end

hold off



143

APPENDIX – 8

Scheduling of Individual Routes

function varargout = untitled1(varargin)

% UNTITLED1 MATLAB code for untitled1.fig

% UNTITLED1, by itself, creates a new UNTITLED1 or raises the existing

% singleton*.

%

% H = UNTITLED1 returns the handle to a new UNTITLED1 or the handle to


%

% UNTITLED1('CALLBACK',hObject,eventData,handles,...) calls the local

% function named CALLBACK in UNTITLED1.M with the given input arguments.

%

% UNTITLED1('Property','Value',...) creates a new UNTITLED1 or raises the


% applied to the GUI before untitled1_OpeningFcn gets called. An


% stop. All inputs are passed to untitled1_OpeningFcn via varargin.

%



%


% Edit the above text to modify the response to help untitled1



gui_Singleton = 1;



'gui_OpeningFcn', @untitled1_OpeningFcn, ...

144

'gui_OutputFcn', @untitled1_OutputFcn, ...





end

if nargout


else


end


% --- Executes just before untitled1 is made visible.

function untitled1_OpeningFcn(hObject, eventdata, handles, varargin)





% varargin command line arguments to untitled1 (see VARARGIN)

% Choose default command line output for untitled1










145



% UIWAIT makes untitled1 wait for user response (see UIRESUME)



function varargout = untitled1_OutputFcn(hObject, eventdata, handles)







% --- Executes on selection change in popupmenu1.

function popupmenu1_Callback(hObject, eventdata, handles)

% hObject handle to popupmenu1 (see GCBO)



cla(handles.axes1,'reset');

[ALL,ALL1]=xlsread('C_K_T.xlsx')

%switch case has been used for the Each routes with different Route kilometer, route cost and route time

switch get(handles.popupmenu1,'Value')

case 1

X=xlsread('Routing_data.xlsx','Ahm_1')

[TA,TA1]=xlsread('Station_sheet.xlsx','Ahm_1')

KMS=ALL(1,1)

CST=ALL(1,3)

TYM=ALL1(2,3)

case 2



146

KMS=ALL(2,1)

CST=ALL(2,3)

TYM=ALL1(3,3)

case 3



KMS=ALL(3,1)

CST=ALL(3,3)

TYM=ALL1(4,3)

case 4

X=xlsread('Routing_data.xlsx','Meh_1')

[TA,TA1]=xlsread('Station_sheet.xlsx','Meh_1')

KMS=ALL(4,1)

CST=ALL(4,3)

TYM=ALL1(5,3)

case 5


[TA,TA1]=xlsread('Station_sheet.xlsx','Meh_2')

KMS=ALL(5,1)

CST=ALL(5,3)

TYM=ALL1(6,3)

case 6

X=xlsread('Routing_data.xlsx','Raj_1')

[TA,TA1]=xlsread('Station_sheet.xlsx','Raj_1')

KMS=ALL(6,1)

CST=ALL(6,3)

TYM=ALL1(7,3)

case 7



KMS=ALL(7,1)

CST=ALL(7,3)

TYM=ALL1(8,3)

case 8

147



KMS=ALL(8,1)

CST=ALL(8,3)

TYM=ALL1(9,3)

case 9



KMS=ALL(9,1)

CST=ALL(9,3)

TYM=ALL1(10,3)

case 10

X=xlsread('Routing_data.xlsx','Vad_1')

[TA,TA1]=xlsread('Station_sheet.xlsx','Vad_1')

KMS=ALL(10,1)

CST=ALL(10,3)

TYM=ALL1(11,3)

case 11

X=xlsread('Routing_data.xlsx','Vad_2')

[TA,TA1]=xlsread('Station_sheet.xlsx','Vad_2')

KMS=ALL(11,1)

CST=ALL(11,3)

TYM=ALL1(12,3)

case 12

X=xlsread('Routing_data.xlsx','Sur_1')

[TA,TA1]=xlsread('Station_sheet.xlsx','Sur_1')

KMS=ALL(12,1)

CST=ALL(12,3)

TYM=ALL1(13,3)

case 13

X=xlsread('Routing_data.xlsx','Sur_2')

[TA,TA1]=xlsread('Station_sheet.xlsx','Sur_2')

KMS=ALL(13,1)

CST=ALL(13,3)

TYM=ALL1(14,3)

end

% axes handles for the ploting the figure in a GUI

148

axes(handles.axes1)

plot(X(:, 1), X(:, 2),'r','Marker','.', 'LineWidth', 3.5);

line(X(1, 1), X(1, 2), 'Marker', 's', ...


line(X(end, 1), X(end, 2), 'Marker', 's', ...


xlim([69.50,74.30]);

% Google map%


set(handles.TABLE, 'Data',TA1);

set(handles.TIME1,'String',TYM)

set(handles.COST1,'String',CST)

set(handles.KMS1,'String',KMS)

% Hints: contents = cellstr(get(hObject,'String')) returns popupmenu1 contents as cell array

% contents{get(hObject,'Value')} returns selected item from popupmenu1


function popupmenu1_CreateFcn(hObject, eventdata, handles)

% hObject handle to popupmenu1 (see GCBO)



% Hint: popupmenu controls usually have a white background on Windows.




end



149

APPENDIX – 9

Graphical Representation of Milk-Run Routing

System

function varargout = XPERIMENT(varargin)

% XPERIMENT MATLAB code for XPERIMENT.fig

% XPERIMENT, by itself, creates a new XPERIMENT or raises the existing

% singleton*.

%

% H = XPERIMENT returns the handle to a new XPERIMENT or the handle to


%

% XPERIMENT('CALLBACK',hObject,eventData,handles,...) calls the local

% function named CALLBACK in XPERIMENT.M with the given input arguments.

%

% XPERIMENT('Property','Value',...) creates a new XPERIMENT or raises the


% applied to the GUI before XPERIMENT_OpeningFcn gets called. An


% stop. All inputs are passed to XPERIMENT_OpeningFcn via varargin.

%



%


% Edit the above text to modify the response to help XPERIMENT



gui_Singleton = 1;


150


'gui_OpeningFcn', @XPERIMENT_OpeningFcn, ...

'gui_OutputFcn', @XPERIMENT_OutputFcn, ...





end

if nargout


else


end


% --- Executes just before XPERIMENT is made visible.

function XPERIMENT_OpeningFcn(hObject, eventdata, handles, varargin)





% varargin command line arguments to XPERIMENT (see VARARGIN)

% Choose default command line output for XPERIMENT









151




% UIWAIT makes XPERIMENT wait for user response (see UIRESUME)



function varargout = XPERIMENT_OutputFcn(hObject, eventdata, handles)







function ST_GUI_Callback(hObject, eventdata, handles)

% hObject handle to ST_GUI (see GCBO)



input=get(hObject,'string')

% Hints: get(hObject,'String') returns contents of ST_GUI as text

% str2double(get(hObject,'String')) returns contents of ST_GUI as a double


function ST_GUI_CreateFcn(hObject, eventdata, handles)

% hObject handle to ST_GUI (see GCBO)



152





end

function DT_GUI_Callback(hObject, eventdata, handles)

% hObject handle to DT_GUI (see GCBO)



input=get(hObject,'string')

% Hints: get(hObject,'String') returns contents of DT_GUI as text

% str2double(get(hObject,'String')) returns contents of DT_GUI as a double


function DT_GUI_CreateFcn(hObject, eventdata, handles)

% hObject handle to DT_GUI (see GCBO)







end

function NOV_GUI_Callback(hObject, eventdata, handles)

% hObject handle to NOV_GUI (see GCBO)



input=str2num(get(hObject,'string'))

% Hints: get(hObject,'String') returns contents of NOV_GUI as text

153

% str2double(get(hObject,'String')) returns contents of NOV_GUI as a double


function NOV_GUI_CreateFcn(hObject, eventdata, handles)

% hObject handle to NOV_GUI (see GCBO)







end

% --- Executes on button press in CAL.

function CAL_Callback(hObject, eventdata, handles)

% hObject handle to CAL (see GCBO)



cla(handles.axes1,'reset');

st=get(handles.ST_GUI,'string');

dt=get(handles.DT_GUI,'string');

Vehicle=[get(handles.NOV_GUI,'string')-48];


major01=xlsread('city_name.xlsx','All')



[Major_Name,Major_kms]=xlsread('Major kms.xlsx')

[ans,ans01,ans02]=city_name_st(st)

st_cl=ans01

154

st_rw=str2num(ans02)

st_name=ans

[ans,ans01,ans02]=city_name_dt(dt)

dt_cl=ans01

dt_rw=str2num(ans02)

dt_name=ans

% Defigning Starting route Data ...................................................%

st01=st_name(1:3)

sheetnoS=st_name(5)

[x,y]=xlsread('city_name.xlsx',st01)

st_row=y(:,st_rw)

st_route=st_row(st_cl:length(st_row))

% Defigning the Destination route data..................................................%

dt01=dt_name(1:3)

sheetnoD=dt_name(5)

[x1,y1]=xlsread('city_name.xlsx',dt01)

dt_row=y1(:,dt_rw)

dt_route=dt_row(1:dt_cl)

if strcmpi(dt01,'ahm')==1

MAJOR_dt=major(1)

elseif strcmpi(dt01,'meh')==1

MAJOR_dt=major(2)

elseif strcmpi(dt01,'vad')==1

MAJOR_dt=major(3)

elseif strcmpi(dt01,'sur')==1

MAJOR_dt=major(4)

elseif strcmpi(dt01,'raj')==1

MAJOR_dt=major(5)

end

% ............MAJOR DESTINATION MATCHING..................................................%

if strcmpi(st01,'ahm')==1

MAJOR_st=major(1)

155

elseif strcmpi(st01,'meh')==1

MAJOR_st=major(2)

elseif strcmpi(st01,'vad')==1

MAJOR_st=major(3)

elseif strcmpi(st01,'sur')==1

MAJOR_st=major(4)

elseif strcmpi(st01,'raj')==1

MAJOR_st=major(5)

end

% .... MAJOR CITY ROUTING DATA EXCEL SHEET.........................................................%

Mejor_city=strcat(st01,'_',dt01)

MJR_XL=xlsread('Regional.xlsx',Mejor_city)

route_sheet=xlsread('ROUTE SHEET.xlsx',st_name)

st_num_S=num2str(route_sheet(st_cl,2))

L0=length(route_sheet)

st_num_E=num2str(route_sheet(L0,2))

RANGE_S=num2str(strcat('A',st_num_S,':','B',st_num_E))

ST_XL=xlsread('Routing_data.xlsx',st_name,RANGE_S)

% Major cities for the Destination cities..............................%

route_sheet=xlsread('ROUTE SHEET.xlsx',dt_name)

dt_num_D1=num2str(route_sheet(dt_cl,2))

RANGE_D=num2str(strcat('A1',':','B',dt_num_D1))

DT_XL=xlsread('Routing_data.xlsx',dt_name,RANGE_D)

Origin_st_data=[ST_XL;MJR_XL;DT_XL]

% Axes handles for the ploting the figure in the GUI .................................%

axes(handles.axes1)

plot(Origin_st_data(:, 1), Origin_st_data(:, 2),'r','Marker','.', 'LineWidth', 3.5);

line(Origin_st_data(1, 1), Origin_st_data(1, 2), 'Marker', 's', ...


line(Origin_st_data(end, 1), Origin_st_data(end, 2), 'Marker', 's', ...


156

xlim([69.50,74.30]);

% Ploting the data in the Google map %


guidata(hObject,handles)

%comparing the major starting cities with the available names %

[Major_Kms,Major_Name]=xlsread('Major kms.xlsx')

if strcmpi(MAJOR_st,Major_Name(1))==1

Q=1


Q=2


Q=3


Q=4


Q=5

end

if strcmpi(MAJOR_dt,Major_Name(1))==1

O=1


O=2


O=3


O=4


O=5

end

M_KMS=Major_Kms(Q,O)

St_range_cost=xlsread('All Routes kms.xlsx',st_name)

st_KMS=St_range_cost(st_cl,3)

Dt_range_cost=xlsread('All Routes kms.xlsx',dt_name)

dt_KMS=Dt_range_cost(dt_cl,2)

157

% Defining the total Kilo meters

Total_KMS=st_KMS+dt_KMS+M_KMS

% Defining the Vehical cost

Vehicle_cost=Vehicle*330

% Calculating the Travelling cost

Travelling_cost=Total_KMS*8

T_K=num2str(Total_KMS)

SUB_TIME=Total_KMS/50

TOTAL_HOUR=fix(SUB_TIME)

TOTAL_HOUR1=num2str(TOTAL_HOUR)

TOTAL_MIN=num2str(round((SUB_TIME*60)-(TOTAL_HOUR*60),0))

Total_cost=Vehicle_cost+Travelling_cost

Veh=num2str(Vehicle)

T_C=num2str(Total_cost)

%seting the handles in the GUI for representation

set(handles.ANSR_GUI,'string',Total_cost)

set(handles.TIME1_GUI,'string',TOTAL_HOUR1)

set(handles.TIME2_GUI,'string',TOTAL_MIN)

set(handles.KMS_GUI,'string',Total_KMS)



158

APPENDIX – 10

Construction of Express Routing

% Loading of the express route

Data006=xlsread('E01.xlsx','All')

Data007=xlsread('E01.xlsx','All_1')

% plot route data


hold on

% plot route data


xlim([69.50,74.30]);

ylim([20.4,24.6]);

% Ploting of the Google map on figure


%Plotting coloures .................................................%

Red='r'

Magenta='m'

Cyan='c'

Green='g'

C01=rgb('Green')

C02=rgb('OrangeRed')

C03=rgb('Salmon')

C04=rgb('DarkRed')

%......................... ROUTE SHEET ...............................%




159











% ......................... ACTUAL ROUTING DATA............... %














% ................... Actual coding for the configeration of the route........................................%

Xpres={'Ahm011','Ahm021','Ahm031','Meh011','Meh021','Raj011','Raj021','Raj031','Raj041','Sur011','Sur021','V

ad011','Vad021'}

for k=1:length(Xpres)

hold on

O=Xpres(k)

O1=char(O)

w01=O1(1:3)

w02=O1(5)



160

plot(Universal_data(:, 1), Universal_data(:, 2),'color',rgb('Crimson'),'Marker','.', 'LineWidth', 3.5);

xlim([69.50,74.30]);

ylim([20.4,24.6]);

end

hold on

for i=1:length(Xpres)

hold on

O=Xpres(i)

O1=char(O)

w01=O1(1:3)

w02=O1(5)



RS=xlsread('ROUTE SHEET.xlsx',X)

[r,c]=size(RS)

for j=1:(r-1)

Weight=RS(j,3)

X1=num2str(RS(j,2))

Y=num2str(RS(j+1,2))

Range=strcat('A',X1,':','B',Y)

Origin_st_data=xlsread('Routing_data.xlsx',X,Range)

if Weight<175

plot(Origin_st_data(:, 1), Origin_st_data(:, 2),'color',rgb('LightGreen'),'Marker','.', 'LineWidth', 3.5);

xlim([69.50,74.30]);

ylim([20.4,24.6]);

% Google map



plot(Origin_st_data(:, 1), Origin_st_data(:, 2),'color',rgb('Yellow'),'Marker','.', 'LineWidth', 3.5);

xlim([69.50,74.30]);

ylim([20.4,24.6]);

161

% Google map



plot(Origin_st_data(:, 1), Origin_st_data(:, 2),'color',rgb('Orange'),'Marker','.', 'LineWidth', 3.5);

xlim([69.50,74.30]);

ylim([20.4,24.6]);

% Google map


elseif Weight>700

plot(Origin_st_data(:, 1), Origin_st_data(:, 2),'color',rgb('Crimson'),'Marker','.', 'LineWidth', 3.5);

xlim([69.50,74.30]);

ylim([20.4,24.6]);

% Google map


end

end

end

hold off



162

APPENDIX – 11

Development of Dynamic Milk-Run Material

Supply System

function varargout = Dynamic(varargin)

% DYNAMIC MATLAB code for Dynamic.fig

% DYNAMIC, by itself, creates a new DYNAMIC or raises the existing

% singleton*.

%

% H = DYNAMIC returns the handle to a new DYNAMIC or the handle to


%

% DYNAMIC('CALLBACK',hObject,eventData,handles,...) calls the local

% function named CALLBACK in DYNAMIC.M with the given input arguments.

%

% DYNAMIC('Property','Value',...) creates a new DYNAMIC or raises the


% applied to the GUI before Dynamic_OpeningFcn gets called. An


% stop. All inputs are passed to Dynamic_OpeningFcn via varargin.

%



%


% Edit the above text to modify the response to help Dynamic



gui_Singleton = 1;



'gui_OpeningFcn', @Dynamic_OpeningFcn, ...

'gui_OutputFcn', @Dynamic_OutputFcn, ...





end

if nargout


else

163


end


% --- Executes just before Dynamic is made visible.

function Dynamic_OpeningFcn(hObject, eventdata, handles, varargin)





% varargin command line arguments to Dynamic (see VARARGIN)

% Choose default command line output for Dynamic




% UIWAIT makes Dynamic wait for user response (see UIRESUME)



function varargout = Dynamic_OutputFcn(hObject, eventdata, handles)







function goz_Callback(hObject, eventdata, handles)

% hObject handle to goz (see GCBO)



% Hints: get(hObject,'String') returns contents of goz as text

% str2double(get(hObject,'String')) returns contents of goz as a double


function goz_CreateFcn(hObject, eventdata, handles)

% hObject handle to goz (see GCBO)







164

end

function vis_Callback(hObject, eventdata, handles)

% hObject handle to vis (see GCBO)



% Hints: get(hObject,'String') returns contents of vis as text

% str2double(get(hObject,'String')) returns contents of vis as a double


function vis_CreateFcn(hObject, eventdata, handles)

% hObject handle to vis (see GCBO)







end

function vad_Callback(hObject, eventdata, handles)

% hObject handle to vad (see GCBO)



% Hints: get(hObject,'String') returns contents of vad as text

% str2double(get(hObject,'String')) returns contents of vad as a double


function vad_CreateFcn(hObject, eventdata, handles)

% hObject handle to vad (see GCBO)







end

function khe_Callback(hObject, eventdata, handles)

% hObject handle to khe (see GCBO)



165

% Hints: get(hObject,'String') returns contents of khe as text

% str2double(get(hObject,'String')) returns contents of khe as a double


function khe_CreateFcn(hObject, eventdata, handles)

% hObject handle to khe (see GCBO)







end

function sat_Callback(hObject, eventdata, handles)

% hObject handle to sat (see GCBO)



% Hints: get(hObject,'String') returns contents of sat as text

% str2double(get(hObject,'String')) returns contents of sat as a double


function sat_CreateFcn(hObject, eventdata, handles)

% hObject handle to sat (see GCBO)







end

function pal_Callback(hObject, eventdata, handles)

% hObject handle to pal (see GCBO)



% Hints: get(hObject,'String') returns contents of pal as text

% str2double(get(hObject,'String')) returns contents of pal as a double


function pal_CreateFcn(hObject, eventdata, handles)

% hObject handle to pal (see GCBO)

166







end

function sid_Callback(hObject, eventdata, handles)

% hObject handle to sid (see GCBO)



% Hints: get(hObject,'String') returns contents of sid as text

% str2double(get(hObject,'String')) returns contents of sid as a double


function sid_CreateFcn(hObject, eventdata, handles)

% hObject handle to sid (see GCBO)







end

function unj_Callback(hObject, eventdata, handles)

% hObject handle to unj (see GCBO)



% Hints: get(hObject,'String') returns contents of unj as text

% str2double(get(hObject,'String')) returns contents of unj as a double


function unj_CreateFcn(hObject, eventdata, handles)

% hObject handle to unj (see GCBO)







end

167

% --- Executes on button press in plotter.

function plotter_Callback(hObject, eventdata, handles)

% hObject handle to plotter (see GCBO)



goz=str2double(get(handles.goz,'String'))

vis=str2double(get(handles.vis,'String'))

vad=str2double(get(handles.vad,'String'))

khe=str2double(get(handles.khe,'String'))

sat=str2double(get(handles.sat,'String'))

pal=str2double(get(handles.pal,'String'))

sid=str2double(get(handles.sid,'String'))

unj=str2double(get(handles.unj,'String'))

A=[goz,vis,vad,khe,sat,sat,pal,sid,unj]

A1=A(1)+A(2)

A2=A1+A(3)

A3=A2+A(4)

A4=A3+A(5)

A5=A4+A(6)

A6=A5+A(7)

A7=A6+A(8)

A8=A7+A(9)

if goz<200


T_K=212

T_T='6 hr 45 min'

T_C=2026

elseif sat<200


T_K=202

T_T='6 hr 15 min'

T_C=1946

else


T_K=241

T_T='7 hr 25 min'

T_C=2258

end

axes(handles.plot)

plot(X(:, 1), X(:, 2), 'g', 'LineWidth', 2);

line(X(1, 1), X(1, 2), 'Marker', 'o', ...


line(X(end, 1), X(end, 2), 'Marker', 's', ...


xlim([70.50,74.30]); axis equal off

set(handles.KMs,'String',T_K)

set(handles.TIME,'String',T_T)

168

set(handles.COST,'String',T_C)

% Google map


guidata(hObject,handles)

% --- Executes on button press in radiobutton1.

function radiobutton1_Callback(hObject, eventdata, handles)

% hObject handle to radiobutton1 (see GCBO)



% Hint: get(hObject,'Value') returns toggle state of radiobutton1



DESIGN AND OPTIMIZATION OF MILK-RUN …gtu.ac.in/uploads/1Dipteshkumar...

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