A Framework for the Integration of Assembly Planning and Material Handling Decision

7/30/2019 A Framework for the Integration of Assembly Planning and Material Handling Decision

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A Framework for the Integration of Assembly Planning and Material Handling Decision

Sonia M. Bartolomei-Surez

Department of Industrial EngineeringUniversity of Puerto Rico-Mayaguez

Box 5000

Mayaguez, Puerto Rico 00681-5000

Pius J. Egbelu

Department of Industrial & Manufacturing Systems Engineering

Iowa State University

Ames, Iowa 50011

AbstractMost finished products leave the factory floor in the form of final assemblies consisting of

components and subassemblies. The planning and design of a product assembly system is a

complex operation that offers opportunities for efficiency but one that can negatively impact

production if poorly done. This is because product assembly decisions affect shop floor material

flow pattern, assembly sequence selection, assembly task clustering, task cluster assignment to

stations, material handling, unit load size specification, and assembly stations layout. In this

paper, a framework for integrating assembly systems planning in a low to medium volume

production environment is presented.

Key words:

Assembly plan, subassemblies, clusters, material handling, assembly sequence

1. Introduction

Introduce the paper by discussing the problem of assembly planning in a manufacturing

environment where the flow line type of assembly station layout is inappropriate. This is

particularly so in batch manufacturing environment where a large variety of products are

manufactured and assembled and no product has a volume of flow large enough to justify a flow

line type layout of assembly stations. In such production systems, an appropriate layout of the

assembly stations is a layout that is flexible enough accommodate the individual flow patterns of

the various products assembled but also reduces material handling cost and improves on the

flows. This implies that the layout must allow the products to follow their individual assembly

sequence requirements but share common assembly stations


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The ability to realize an assembly system layout that accommodates the assembly of multiple

products while minimizing material flow cost and consequently, the actual assembly cost is

dependent on the adequacy of the assembly system planning and design process employed and

how the various factors that affect assembly cost are integrated together. Assembly planning is

defined as the actions of task decomposition, reasoning, and program generation that precede any

physical assembly work. Assembly planning can be decomposed into several activities.Assembly systems planning involves the following tasks :

a) assembly sequence generation

b) assembly sequence evaluation and selection

c) grouping or clustering of assembly tasks into logical operations

d) balancing the work content between the assembly task clusters

e) identification of the sites or locations on which the assembly stations are to be

placed

f) defining the unit load sizes (i.e., number of parts to be handled per trip for the

components and subassemblies as they are transferred from a preceding station to a

succeeding station.

g) assignment of the assembly task groups to the sites or location to reduce materialhandling cost and improve on the overall system efficiency

Most reported work on assembly planning only address problems (a) through (e) and completely

ignore problems (f) and (g). On the other hand, material handling problems often concentrate on

problem (f) and (g) and implicitly assumes that problems (a) through (e) have been resolved. As

it will be discussed latter in the paper, such separate treatment of assembly systems planning is

inappropriate because it is likely to produce an inferior design. What is required is a concurrent

engineering approach that considers all the essential factors together in some form. In the next

section, we provide a further discussion and justification for system integration.

2. Justification for System Integration

The traditional design approach for an assembly system is sequential. This sequential approach

ignores the interrelationship between the various decision domain as described below:

A typical item for asembly can be assembled using one of several assembly sequences. The

assembly efficiency associated with the various sequences are non-uniform. Generating assembly

sequences is a combinatorial process, and the complexity of the problem increases exponentially

with the total number of component parts involved. Theoritically, for a product consisting of 5

parts, the possible number of assembly sequences is 5! or 120. In practice, some of these

sequences may be infeasible or very difficult to accomplish As an illustration consider theassembly of a toy car. Two assembly plans that successfully assemble this product are as shown

in Figure 1 and Figure 2 respectively. Several other assembly plans can be generated for this

product. The operations grouped together in a set constitute a cluster of tasks to be performed at

one assembly station. Even though these two plans require four workstations to execute, from a

material handling point of view, the material flow patterns they generate on the assembly shop

may be different. Furthermore, the workload balance obtained between these two plans for the


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four workstations may also be different. A natural question a person can ask at this point is which

of these two plans is better? The answer to this question of course depends on on whether the

decision is based only on the workload balance between the stations, on the assembly cycle time

generated, material handling effect, or on the total effect due to the assembly operations,

workload balance, and material handling.

B

C

D

E

(BC)

(DE)

A

((BC),(DE))

F

G

H

IJ

K

L

(FG)

(HI)

(A,(BC),(DE)) FINAL PRODUCT

Cluster 1

Cluster 2Cluster 4

Cluster 3

Figure 1. First Assembly Plan Chosen from the BOM and Group of Assembly

Tasks into Sets


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B

C

D

E

(BC)

(DE)

A

F

G

H

IJ

K

L

(FG)

(HI)

FINAL PRODUCT

Cluster 2

Cluster 4

Cluster 3

Figure 2. Second Assembly Plan Chosen from the BOM and Group of Assembly

Tasks into Sets

Cluster 1

(A,(BC))(A,(BC),(DE))

(A,(BC),(DE)

(FG),(HI))

2.2 Creation of Assembly Task Clusters

In Figures 1 and 2, four operation clusters were defined. Usually, the factor that controls the

maximum total work content assigned to a station is the assembly cycle time. The larger the cycle

time, the higher the allowable total work content per station. Furthermore, the clustering of the

tasks to form valid assembly operations is dependent on both the plan used as well as theclustering algorithm employed. Given the same assembly plan and different clustering

algorithms, it is possible to obtain different operation clusters. This possibility is even higher

when the algorithms encounter a tie between tasks during the clustering process. Given that the

operation clusters translate eventually to different assembly stations, they may impose different

material flow plan on the shop floor. Thus, an assembly plan that may seem attractive from the

point of view of workload balance between stations may have a very negative consequence from

the point of view of material flow pattern.

2.3 Allocation of Assembly Operation Clusters to Assembly Sites

For most realistic assembled products, the assembly line layout is not serial. This is because theassembly process is more like a convergent flow in which subassemblies and components from

different branch assembly lines merge to form new subassembly lines. Once the assembly tasks

have been formed into operation clusters, the network formed by the convergent flows is

dependent on the shop floor locations of the assembly stations required to perform the operation

clusters. The impact of the different flow networks on space utilization may also be different.


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As an illustration of the point raised here, consider the four operation clusters shown in Figure 1.

Furthermore, consider a layout consisting of existing machine stations, MSi and a storage area.

Also appearing on the layout are four locations or sites (AS1, AS2, AS3, and AS4) to which the

four workstations required to assemble the product are to be located. Components required for

assembly come from different locations on the shop as summarized in Table 1. Decision has to

be made as to which operation cluster should be assigned to which site. Figure 3 and Figure 4 arethe material flow pattern generated from two different assignments A and B of the operation

clusters to the four sites, where A = ( C1AS1, C2AS2, C3AS3, C4AS4) and B = (

C1AS4, C2AS3, C3AS1, C4AS2), and Ck = Cluster k.Obviously, these flow patternshave different consequences on the shop.

MS2

MS3

MS1

MS4

AS1

AS2

AS4

AS3

Figure 3. Material Flow Pattern Based On Cluster-Site Assignment #1

Storage


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MS2

MS3

MS1

MS4

STORAGE

AS1

AS2

AS4

AS3

Figure 4. Material Flow Pattern Based On Cluster-Site Assignment #2

Table 1: Raw Material, Components and Subassembly Sources

Raw Material and

Components Source

Stations

Raw Material,

Components, and

Subassemblies

Storage E,K,IMS1 A,H

MS2 B,G

MS3 D,J

MS4 C,F,L

2.4 Material Handling Equipment Selection and Requirement

The selection of a particular type of material handling equipment to support a given operation is

influenced by the material flow pattern, the distances between points of travel, physical

characteristics (e.g., shape, dimensions, weight) of the objects to be handled, and the volume offlow. Different assembly plans create different subassemblies that differ in physical

characteristics. These differences translate to different equipment needs and selection. Thus, the

adoption of an assembly plan and a set of operation clusters for the assembly automatically

constraints the range of material handling equipment that can be explored for selection. In a

similar manner, the assignment of the assembly clusters to sites generates different travel

distances between communicating clusters. The total distance directly determine the number of

units (i.e., fleet size for mobile systems) of the material handling device that has to be acquired


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and its operating parameters (e.g., speed). The interrelationships between assembly plan

selection, assembly task clustering, subassembly shapes and weight, and the assignment of the

clusters to assembly sites make it all the more necessary that these assembly planning and

material handling system design be integrated to design an efficient system.

2.5 Total Assembly Cost

The cost associated with the assembly of a product must account for both the cost of the

assembly process and the cost of material handling. The ease and cost of executing an assembly

task is dependent on the spatial relationship between the mating components. Different assembly

plans generate different assembly sequences and consequently different mating relationships and

cost. The same is true of material handling cost associated with different assembly plans. A plan

that has high assembly process cost may on the other hand have a lower material handling cost

and vice versa. Since it is generally not easy to demonstrate that a particular combination of

assembly plan, assembly tasks clustering, and the arrangement of the workstations dominates all

other combinations, it is necessary that a systematic technique be used to determine the best

decision in any given design scenario. The need to minimize the total cost provides opportunitiesfor optimization. This opportunities are yet to be explored by the manufacturing community.

3. Model of an Integrated Assembly System Platform

As presented in the last section, decisions made at various stages of an assembly system design

process have propagation effects. Therefore, an integrated design system provides one avenue to

seek for an optimal design. Such an integrated system framework is as shown in Figure 5. The

system is composed of multiple decision modules to address the different facets of the problem.

Depending on whether the design is for an existing facility or new facility, input data regarding

existing material handling system must be provided. Otherwise, input data on alternative material

handling systems and their associated unit time operating costs are required. Other required inputdata include information on the product design, the mating relationships between the

components, assembly cycle time, the site within the facility the components are located. Output

from the system include (a) the assembly plan selected, (b) the clustering of the assembly tasks,

(c) the assembly sequence associated with the plan and clusters, (d) the assignment of the

operation clusters to the assembly sites, and (e) a measure of the effectiveness of the design

decision (e.g., total cost)


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Existing Existing

Material FacilityHandling Layout

F

e

e

d

b

ac

k

Assembly Sequence

Generation System

Select the Minimum CostAllocation

Output with the Lowest

Cost PlanStop

Any

Other Alternative Yes

Sequence?

No

Determine Cost Associated with each

Allocation of Assembly Task Group s

to Stations in Each Assembly Sequence

Identify M ajor Sources

of Cost For Elimination

or Reduction

Allocation of Assembly

Task Groups to Stations Document Major

Sources of Cost

Generate another

Assembly Sequence

Grouping Assembly Tasks

into Sets

Any

Other Allocation

Alternative?

NoYes

Re-allocate Assembly

Task Groups to Stations

Product

Design

Process

Design

I

N

P

U

T

D

A

T

A

I

N

T

E

G

R

A

T

I

O

N

P

R

O

C

E

S

S

O

U

T

P

U

T

Figure 5 . Basic Algorithmic Structure to Integrate of Assembly System Design

and Material Handing Design Decisions.[2]


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4. Algorithmic Requirements of an Integrated Assembly Planning and Material Handling

System

To be operational, an integrated assembly planning and material handling design system as

described above must be supported with efficient algorithms for decision making as the process

moves from one decision module to another. In particular, algorithms are required for thefollowing decisions:

a) generation of a subset of potentially good assembly plans

b) clustering of assembly tasks

c) dynamic subassembly shape evaluation subsystem

d) in-plant volume of flow

e) generation of a subset of potential material handling system

f) assignment of operation clusters to assembly site based on some measure of

performance

Each of these requirements will be addresses further below.

4.1 Generation of a Subset of Potentially Good Assembly Plans

As was mentioned earlier, given a product with n components, if a serial assembly process is

adopted, there can be up to n! possible assembly sequences for the product. However, for most

practical situations, many of these plans will be infeasible and are therefore, not worthy of

generation. Furthermore, if the assembly plan include parallel branches, the number of assembly

sequences that need to be evaluated is reduced. For an efficient integrated system, what is

required is an algorithm that can implicitly evaluate the product tree to generate a fixed set of

potentially good assembly plans from which one is adopted. Reported research work [4,5,6] on

assembly plan generation have not focused on developing algorithms that implicitly and

exhaustively search the product tree to generate only the potentially efficient assembly plans.

Therefore, what is needed are efficient assembly plan generation system that generates a smallpopulation of assembly plans that has a high probability of containing the optimal plan.

4.2 Clustering of Assembly Tasks

A cluster of assembly tasks is a group of tasks performed and assigned to an assembly station.

The sum of the times required to execute all tasks assigned to a station defines the work content

of the station per unit final part. Assembly task clustering is essentially the classic problem

addressed in assembly line balancing in which the assembly tasks are distributed to the assembly

stations in a logical manner that facilitates the assembly of the final product. Assembly task

clustering is driven by the rate at which the final product or assemblies are to be completed. The

higher the rate, the lower should be the work content assigned to a station and vice versa. Given a

production rate, Rj, for product j, the assembly cycle time, Tj, in minutes is calculated as Tj =

60/Rj. It is this cycle time that controls the amount of work assigned to a station. The value of the

assembly cycle time represents the maximum work content that can be assigned to a station.


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For the purpose of the integrated assembly planning and material handling system, advantage can

be taken of existing assembly line balancing algorithms [3,7]. However, enhancement would be

needed to these algorithms to allow for the generation of alternative operation clusters when

several tasks are candidates for consideration to cluster assignment during the execution of the

algorithmic steps. Assembly line balancing algorithms with capability to simultaneously generate

and evaluate several alternative assembly tasks clusters are not yet available in the publicdomain.

4.3 Dynamic Subassembly Shape Evaluation Subsystem

An essential information required for the planning for assembly and material handling is

knowing the physical characteristics of the items to be assembled and handled. For both

assembly system design and material handling system design, knowing the shape of the object is

very critical. For assembly system design, the shape and weight are used in determining where to

grasp the part during assembly, the orientation for pickup and insertion on the base assembly

object, the required grasping force, and in the design of the work holding fixtures. Knowing the

design requirements for fixtures is necessary for assembly cost estimation. Similarly, for materialhandling, the shape is used for determining how best the object is to be unitized and transferred.

The unitization and transfer decisions involve determining the fixtures, containers and pallet

requirements for transferring components and subassemblies between stations, the potential

candidate transfer equipment, and the width and characteristics of the material transfer paths. The

physical characteristics data is also required to assess whether the existing material handling

equipment is adequate to serve the need. If existing handling equipment is inadequate, a new type

of material handling system would have to be considered. To date, the authors are unaware of

any general purpose algorithms that can dynamically estimate the shape of subassemblies for

planning purposes.

4.4 In-plant Volume of Flow

The flow volume of a product within the shop floor is not necessarily the demand volume. For an

item j, the material flow volume, Qjik, between workstations i and k is equal to Vj/qjik, where Vjis the number of units of item j to be produced and qjik is the unit quantity of the item

transported per trip between workstations i and k. The value for qjik is dependent on the

geometry of item j, its unit weight, restrictions on transport weight and volume, and the fragility

of the item. Through careful design of the operation clusters, the change in geometric volume and

weight of subassemblies built during the initial assembly stages can be controlled. An assembly

process in which the large and heavy component items and subassemblies are required in the

early stages of the assembly process will also end up building large and heavy subassemblies

very early on in the assembly process. In situations where large subassemblies are built during

the early stages of assembly, the number of trips and consequently, the total distance required to

handle subassemblies will also increase. The reverse is also true. If use of large and heavy

components are delayed until the latter assembly stages, the number moves required to handle the

subassemblies will also decrease. Of course, the volume and weight of the end product is the

same regardless of the assembly plan used. For this reason, an integrated assembly and material

handling system must be equipped with intelligent algorithm that selects assembly plans and


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builds operation clusters that optimize on the both the physical assembly process as well as the

volume and weight of intermediate subassemblies. By optimizing on the physical characteristics

of intermediate subassemblies, the cost of material handling can be minimized. At present, no

algorithm with such a global view is in known.

4.5 Generation of a Subset of Potential Material Handling System

The type of material handling equipment required is influenced by the material transfer volume

and the distances between communicating stations. Therefore, the type of material handling

equipment required is indirectly influenced by the factors that also affect the transfer volume. In a

new facility, these factors together can be used to generate a database of potential material

handling equipment that can be used to convey the component items and intermediate

subassemblies from their sources to destinations. The type of information needed at this stage are

similar to those found in Apple [1]. Similar information is required for existing facilities except

the database has to include existing handling equipment as well. From this database, the best

material handling equipment is selected for each transfer link.

4.6 Assignment of Operation Clusters to Assembly Sites Based on Some Measure Of

Performance

The algorithm that is required at this stage is one that assigns the operation clusters to assembly

sites and selects the appropriate material handling equipment for each transport link. The

problem can be modeled as a special form of the quadratic assignment problem with n new

facilities and n sites. The facilities are the operation clusters and the sites are the candidate

assembly station locations. The flow between the n stations are the subassemblies generated by

the clusters. In addition to the new flows generated by the subassemblies, there are also the flow

of component items or raw materials from the warehouse and machine stations to the assembly

stations. The warehouse and the machine stations can be viewed as existing workstations. Anappropriate model at this stage can be represented as described below

Let

Cikjh = the cost of handling all items between new facilities i and jthrough a unit distance,if facility i is located at site kand facilityj at site h

tij = the flow volume between new facilities i and j

dkh = the distance between sites kand h

kjh = the cost per move per unit distance of handling items between new facilities i andj

ifi is assigned to site kand j to site h.

Cgil

= the cost of handling between existing facility g and new facility i, given that facility

i is assigned to site l

tgi = the flow volume between existing facility g and new facility i

dgl = the distance between existing facility g and site l

Vgil = cost per move per unit distance between existing facility g and i given that i is

assigned to site l

From the definitions above,


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Cijkh = Wijkhtij (1)

Cgil = Vgil tgi (2)

The model reflecting the total material handling cost with recognition to material handling

system requirement changes as a function of changes in distances between facilities is as given inproblem P1.

P1: Min f C d X C d X Xk

n

gik gk ik

i

n

g

m

h

n

j i

n

ijkh kh ik jh

k

n

i

n

= +=== == +==

111 1111

1

' (3)

s.t. X k niki

n

= ==

1 1 21

, , ,......., (4)

X i nikk

n

= ==

1 1 21

, , ,......., (5)

x or egerij = 0 1, int (6)

The first term on the right of the equality sign in equation (3) models the flow of the raw

materials or components from the existing workstations and warehouse to the assembly stations.

The second term models the flow of subassembly items between the assembly stations. Equation

(4) ensures that only one assembly cluster is assigned to each assembly location. Equation (5)

ensures that every operation cluster is assigned to an assembly site. Equation (6) is the integrality

constraint. Problem P1 is an integer model and can be solved using integer programming

algorithms. It should be noted that this model needs to be reconstructed and solved during each

iteration of integrated system. Because, the model may be invoked several times during a design

process, an efficient method to solve problem P1 either optimally or near optimally using aheuristic algorithm is required.

The problem of designing an integrated assembly and material handling systems design is a

challenging problem that definitely can benefit from improved computational solution

algorithms. The improved computational speed of computers today makes the realization of an

integrated design platform hopeful.

5. Conclusion

In this paper, effort was made in highlighting the benefits of an integrated product assembly and

material handling systems design in a manufacturing environment. A general framework ofsearch an integrated system was presented along with opportunities for algorithm development.

Integration and joint resolution of closely interrelated manufacturing problems offer great

opportunities for cost reduction and hold the key for maintaining global competitiveness.

References

[1] Apple, J.M., Material Handling Systems Design, Wiley, New York, 1972.


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[2] Bartolomei-Surez, Integration of Assembly Planning and Material Handling, Ph.D.

Dissertation, Department of Industrial and Manufacturing Engineering, Pennsylvania State

University, State College, Pennsylvania, 1996.

[3] Dar-El, E.M., Solving Large Single-Model Assembly Line Balancing Problem - AComparative Study, AIIE Transactions Vol.7, No.3, 1975, pp.302-310.

[4] De Fazio, T.L. and Whitney, D.E., Simplified Generation of All Mechanical assembly

Sequences, IEEE Journal of Robotics and Automation, Vol. RA--3/6, 1987, pp.640-658.

[5] Ko, H. and Lee, K., Automatic Assembly Procedure Generation from Mating Conditions,

CAD Vol.19, No.1, 1987, pp.3-10

[6] Lee, C-H and Egbelu, P.J., Automatic Generation of Assembly Sequences by Constraint-

Solving Approach, Working Paper, Department of Industrial and Management Systems

Engineering, Pennsylvania State University, State College, PA, 1993.

[7] Mastor, A.A., An Experimental Investigation and Comparative Evaluation of Production

Line Balancing Techniques, Management Science, Vol.16, No.11, 1970, pp.728-746.

A Framework for the Integration of Assembly Planning and Material Handling Decision

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