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Robotics and Computer-Integrated Manufacturing 23 (2007) 608–613

www.elsevier.com/locate/rcim

Multi-plant production scheduling in SMEs

Esther Alvarez�

Faculty of Engineering, Organization Department, University of Deusto, Apdo. 1, 48080 Bilbao, Spain

Abstract

Traditionally, companies used to view themselves as separate entities and did not devote efforts to collaborate with other echelons of

the extended enterprise. This even happened at multi-site companies owning different plants that belonged to the same supply chain.

However, manufacturing facilities of the same supply chain should intensively share information and coordinate planning and scheduling

tasks in order to get a globally optimized solution. This paper discusses some specific characteristics of the planning and scheduling

problem in the extended enterprise including an analysis of a case study, and reviews the available state-of-the-art research studies in this

field. Most studies suggest that integrated approaches can have a significant impact on the system performance, in terms of lower

production costs, and less inventory levels. The paper provides some conclusions and suggestions of future works.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Collaborative planning; Multi-plant dynamic scheduling

1. Introduction

A supply chain is an extended enterprise, whichencompasses a series of agents, such as vendors, whole-salers, manufacturers, retailers and customers. Thesecompanies must share information and coordinate physicalexecution to ensure a smooth flow of goods, services,information, and cash through the pipeline. Collaborationamong entities in the supply chain can have a great impacton the system performance. The ability to develop effectivecollaborative planning in the extended enterprise willbecome a key to success in the future.

In order to remain competitive in the market, manycompanies must examine alternative solutions for theirlogistics network. This process allows for gaining insightinto the cost or service implications of the different availablealternatives. At this point it is necessary to consider manydifferent factors, such as proximity to markets, availabilityof skilled workforce in case of high-tech products, cost oflabour force in labour-intensive manufacturing industries,or transportation and communication costs. But theimportance of these location determinants highly dependson each individual business [6].

e front matter r 2007 Elsevier Ltd. All rights reserved.

im.2007.02.006

4 139 361; fax: +34 944 139 101.

ess: ealvarez@eside.deusto.es.

This means that many companies are often shifting fromone-site manufacturing facilities to multi-site factories inorder to gain competitive advantage in the internationaleconomic arena. When the manufacturing network iscomposed of factories that are able to make differentcomponents, it is possible that a certain part can bemanufactured at different plants. This aspect adds animportant complexity factor at the planning and schedul-ing level. Once a job is allocated to a factory, it is usuallyuneconomical to transfer it to other factories [4], butunexpected events, such as machine failures or operators’absence may justify the re-allocation of jobs to otherfactories in real time.This paper is organized as follows: Section 2 describes the

planning and scheduling problem at the extended enter-prise, including a case study for a multi-site single-ownedmanufacturing network, Section 3 encompasses differentcontributions to this problem, and Section 4 provides someconcluding remarks and future research lines.

2. The planning and scheduling problem at the extended

enterprise, a case study

Manufacturing resources have finite capacity. Thismeans that it is necessary to schedule production regularly,although the scope of this activity is not limited to the

ARTICLE IN PRESSE. Alvarez / Robotics and Computer-Integrated Manufacturing 23 (2007) 608–613 609

manufacturing industry. Production scheduling deals withthe allocation and sequencing of jobs to manufacturingresources over a certain time interval. Its output is aproduction plan, which specifies start and finish times forevery job on each machine. Jobs are scheduled according tosome optimization criteria and the quality of schedules isevaluated through some performance measures related tocosts, resources utilization and due dates fulfilment. Thescheduling problem is complex, as it has to cope with thetechnical difficulty of combinatorial explosion. The generaljob-shop problem consists of n jobs and m machines, so thenumber of possible solutions is (n!)m [1,2].

Modern organizations, in order to survive, must rely onmore flexible and responsive architectures. The marketdynamics demand suitable approaches to reduce inventory,improve the use of machines and encourage customerresponsiveness. As a matter of fact, conflicts derived fromdeviations between assumptions and real data are fixedeither manually or through costly re-planning processes.But these approaches often lead to long productionstoppages diminishing productivity. Furthermore, it isnecessary that these approaches will result in a betterperformance of the extended manufacturing networks.

The concept is focused on companies where the processof production and storage volume is distributed amongseveral sites. A key objective is to find rational tools thatoptimize the production flow over the entire supply chain.This global optimization has not been fully achieved yet,instead each factory in the supply chain is optimizedlocally, which yields to a poor solution.

The standpoint of this research lies in a multi-site SMEcompany that has two manufacturing plants and anassembly plant located within a small geographical area(see Fig. 1). It follows a make-to-order and make-to-stockmixed production strategy.

This figure shows a manufacturing channel that iscomposed of two manufacturing plants that producecomponents and an assembly plant, which connects eitherwith customers or with the market.

The company has a traditional planning and schedulingsoftware system at each plant but it lacks coordinationbetween plants. A prototype of a multi-plant planning andscheduling system has been designed. The main require-

Raw

Raw materialssupply point 1

Raw materialssupply point 2

Raw materialssupply point 3

Manufacturingplant 1

Manufacturingplant 2

Fig. 1. Example of multi-site

ments of this firm as regards planning and scheduling issuesare related to the efficient management of unexpectedevents and the effective coordination of the differentfacilities to obtain a global optimized solution.From the information point of view, this scenario is

composed of a central database that behaves as acoordinating agent and keeps track of the state of thesystem. Besides, there are local databases at each plant thatcontrol the execution of individual production plans. Localdatabases duplicate many data that are located at thecentral database, but at the same time they decentralizemany data to ensure their own autonomy in case ofconnection failure. In fact, WIP data and events aregenerated and controlled at each local plant but thisinformation is periodically uploaded on the centraldatabase. This way each plant continues performingoperations despite the fact that unexpected events canaffect other plants. Distribution of information anddelegation of decisions to local plants are important factorsthat ensure the robustness of the system. The softwarearchitecture to address the aforementioned problem isdepicted in Fig. 2.There are some specific requirements of the company

that are not fulfilled by their current planning andscheduling solution:

�

ma

In addition to machines, operators are critical manu-facturing resources that must be modeled in the system.Besides, there are operators that belong to the samegroup, i.e. having equal qualification that can beallocated to different plants. This means that when anoperator is not available (absent for any reason),operators assigned to other plants might be moved tothe affected plant to substitute this operation. Differentcriteria such as employee’s stoppage times and priorityof work orders must be taken into account in thedecision. But operators should be considered analternative resource as long as they belong to the samegroup.

� It is necessary to synchronize the production schedules

of the different plants. If there is a disruption that causesa delay in the execution of a job at a plant, the relatedwork orders must be rescheduled. So when an assembly

Assemblyplant

(Centralplant)

MarketCustomers

nufacturing company.

ARTICLE IN PRESS

ERP

Production Scheduler Global

DB LocalDB

Shop Floor Data Collection

Shop Floor Data

Collection

Production Scheduler

CENTRAL PLANT LOCAL PLANT

Fig. 2. Multi-plant planning and scheduling schema.

E. Alvarez / Robotics and Computer-Integrated Manufacturing 23 (2007) 608–613610

work order is delayed, the corresponding part workorders should be delayed too. This way in-processinventory will be reduced. In this case, the related partwork orders will have to be rescheduled at the affectedplants.

� Unexpected events related to the unavailability of

resources should be treated from a wide perspective,i.e. considering all alternative resources both in theaffected plant and others. This will enhance theflexibility of the production plan and the probabilityof getting a better global solution. Furthermore, if nosatisfactory solution is obtained with own resources, thepossibility of subcontracting the affected operation willalso be explored. In this respect the use of aperformance-based decision-making system is crucial.

In conclusion, some important challenges have to be facedat industrial applications:

�

Involved constraints: Traditionally, job-shop researchhas only considered constraints related to machines. Butconsidering operators in addition to machines duringthe scheduling process increases the complexity of theproblem exponentially. Sometimes an operation maybecome critical because it requires the participation of ahighly skilled or a scarce type of operator. In fact, for ageneral job-shop problem with n jobs, m machines and k

operators, the total number of possible solutions will be((n!)m)k [16]. Furthermore, with the advent of flexiblemanufacturing systems and just in time manufacturingresearchers and practitioners have recognized theimportance of auxiliary resources (e.g. fixtures andtools) in production activity control and scheduling.

� Details complexity: When there is a manufacturing

network composed of several factories that can havealternative resources, it may happen that a certain itemcan be manufactured at different facilities. This aspectadds a new decision point at the planning andscheduling level. In general, three main issues haveto be solved, i.e. (i) allocation of jobs to suitable plants,

(ii) allocation of jobs to suitable resources at each plant,(iii) determination of the sequence of jobs at eachmanufacturing resource [16]. Once a job is allocated to afactory, it is usually uneconomical to transfer it to otherfactories [4], but unexpected events, such as machinefailures or operators absence may justify the re-allocation of jobs to other factories in real-time, thedynamic subcontract of jobs to third-party companies,or that operators move between factories.

� Real-time reaction: Shop floors are usually too simple in

comparison with real manufacturing plants. They workwith assumptions and constraints that simply do nothold or are overly restrictive in industry. Some of themdeal with the consideration of the manufacturingenvironment as static. But in most real manufacturinginstallations, machines suffer unexpected breakdowns,tools wear away, operations take longer than plannedand changes in production orders happen [19,20].Therefore, a quick reaction to changes is necessary,taking into consideration the significant amount ofindirect costs involved in the derived interruptions.

� Collaborative capabilities: Collaboration among entities

in the supply chain can have a great impact on thesystem performance. In this context, the incorporationof collaborative capabilities to manufacturing systemsplanning and scheduling is an important part of anyenterprise integration effort. Integrated schedulingsystems describe procedures to incorporate and tonegotiate production planning in supply chain manage-ment [3,5,12].

This case study can be easily extended to include morepossibilities and consider a supply-chain owned bydifferent firms.

3. Literature review in distributed planning and scheduling

There has been an important trend in recent years wheremanufacturing firms have become more focused on theircore capabilities and they have tended to contract with

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other specialist firms for the manufacturing of componentsor the provision of services. This trend has enhanced thecompetitiveness of firms that have successfully managedthese manufacturing networks.

Notwithstanding the relevant progress in planning andscheduling techniques over the last years, the situation isstill far from being satisfactory. Some classical and morerecent contributions to the scheduling problem (e.g. integerprogramming, priority dispatching rules, heuristics, con-straint directed scheduling, agent-based systems, or dis-tributed and adaptive control) mean enlighteningcontributions toward a better planning and control system.But an improved methodology for the development of adistributed scheduling system is required to enhance theoptimization and integration aspects that are needed toconform to supply chain requirements. Furthermore, mostcurrent scheduling approaches focus on optimizing pro-duction flow locally.

Centralized production control provides a global view ofthe system but tends to be rigid under dynamic systemenvironments. Since the ownership of a supply chainnetwork is mostly fragmented and the owners havedifferent objectives, the distributed approach to theintegration of production scheduling in supply chains ismore likely to succeed than a centralized approach.Besides, they do not solve large size problems effectively,and provide solutions that degrade very fast.

Chou et al. [5] utilize constraint programming as thecomputation mechanism in collaborative scheduling andplanning. They consider constraints that are limited tounary and binary constraints. Besides, they address theflexibility of production schedules as regards the start timesof their constituent operations, since some of them mightbe moved forward or backward without delaying thecompletion times of all jobs. The authors develop analgorithm to search the slack windows of schedules andapply it to a two-node network. The collaboration processhas two stages. In the first stage, each node passesconstraint or precedence information to a collaborationengine. The engine resolves the conflicts and sends feasiblesolution spaces back to the nodes. Each node then proceedsto optimize the schedule at its domain.

Kanyalkar and Adil [10] consider production planningof multi-site production facilities with substitutable capa-cities serving multiple-selling locations where the supplyingplant is dynamically determined. A linear programmingmodel is developed to produce the time and capacityaggregated plan and the detailed plan simultaneously toovercome the drawback of the hierarchical planningapproaches of not yielding a feasible and/or an optimallower-level plan. Different time grids and planninghorizons for aggregate and detailed planning are appro-priately used to reduce the computational burden. Factorssuch as the limitations of storage space, raw materialavailability and production capacity at plants, as well as arequirement of maintaining a minimum level of inventorybuffer have been modeled. The proposed methodology

generated high-quality production and dispatch planswhen applied to a consumer goods company case.Park [14] presents a heuristic solution for integrating

production and distribution planning that is tested througha computational study in a multi-plant, multi-retailer,multi-item, multi-period logistic environment where theobjective is to maximize the total net profit. Computationalresults show that the integrated planning approachsubstantially outperforms the decoupled one.Chan et al. [4] propose a genetic algorithm approach to

deal with distributed scheduling problems in an alternativeproduction routings environment. The distributed schedul-ing problem mainly involves two issues: (i) allocation ofjobs to suitable factories, and (ii) determination of theproduction scheduling in each factory, considering thatonce a job is allocated to a factory and processed, it isusually unable or uneconomical to transfer this work-in-progress part to another factory for the remainingoperations.Furthermore, there are a number of recent studies that

use agent-based and auction-based systems for implement-ing distributed planning and scheduling systems. A reviewof these recent research studies is presented in the next twosections.

3.1. Agent-based models

Distributed control is characterized by a decompositionof information and decision-making among a group ofloosely coupled controllers, also referred to as agents. Eachagent has local objectives, time-critical data and con-straints. The global system behaviour results from theinteraction of these agents [7]. Fox and coworkers [8,18]addressed the problem of supply chain integration througha network of intelligent agents. Recently, agent technologyhas been considered as an important approach fordeveloping distributed manufacturing planning and sche-duling systems [11].Lu et al. [12] propose an agent-based collaborative

production framework where orders, sub-assemblies, pro-duction lines and cells are modeled as agents that interactwith each other in a collaborative way. The results ofseveral experiments based on simulation show that theirframework achieves a lower cost production.Caridi et al. [3] study the collaborative planning and

forecasting replenishment (CPFR) process and, in parti-cular, the exceptions regarding sales and order forecast bymeans of a multi-agent system. The proposed system iscomposed of an ‘advanced’ model and a ‘learning’ model.The advanced model is able to recognize and dynamicallysolve the exceptions according to the criteria thresholdvalues set in the front-end agreement between partners.The learning model, which is an evolution of theadvanced model, allows the criteria threshold values tobe updated by the model itself through the analysis ofhistorical data. Experimental results indicate better perfor-mance, in terms of total costs (by comparison with CPFR

ARTICLE IN PRESSE. Alvarez / Robotics and Computer-Integrated Manufacturing 23 (2007) 608–613612

without intelligent agents), inventory level and stock-outlevel and sales.

Therefore, agent-based models offer significant advan-tages such as robustness, scalability, reconfigurability, andopen and dynamic architecture [15]. Besides, they can useparallel computation through a large number of proces-sors. But they are also criticized for the unpredictability ofthe system performance from the perspective of the globalobjectives.

3.2. Auction-based systems

Auction is a mechanism that defines a set of rules for theallocation of resources based on bids submitted byparticipants in the auction. From the scheduling point ofview, it is necessary to design a bid structure and a sellingmechanism that would lead to an optimal scheduling ofresources at a shop floor.

Hu et al. [9] present a formal view of coordination usingcontract net protocol (CNP) that relies on the basic loop ofagent behaviours: order receiving, order announcement,bid calculation, and order scheduling followed by orderexecution. They develop a bid calculation model, includinga random parameter set and they build a solution using agenetic algorithm method.

Dewan and Josh [7] developed an auction mechanismthat can be used for scheduling within a distributeddecision-making environment. Their approach utilizedinformation from the global problem while maintainingthe ability to make decisions in a distributed structure.They show that the iterative price adjustment methodreduced resource contention and improved system perfor-mance, despite the job and machines agents solving myopicproblems with local objectives and constraints. Further-more, the proposed distributed model possess improvedreactivity and fault tolerance compared with centralizedmodels, which make them attractive for solving dynamicmodels.

Several implementations of the auction based schedulingapproach apply the use of CNP with various decision-making algorithms such as priority dispatching rules,heuristics and multi-objective function to determine thewinning bidder.

Siwamogsatham and Saygin [17] present an auction-based algorithm for real-time scheduling of flexiblemanufacturing systems with alternate routings. The effec-tiveness of the proposed framework is demonstrated bycomparing it with various priority rules via simulation.

The previous framework was built on a model developedby MacChiaroli and Riemma [13]. The cost function of theoriginal model was modified to incorporate time as theprimary criterion. The coefficients in the cost function werecalibrated to improve the effectiveness of the approach.The model was compared with typical priority rules on thebasis of average tardiness, average lateness, average due-date deviation, utilization balance, average throughput,average wait time and total cost. Analysis of the results

showed that the auction-based approach outperformedpriority rules on most performance measures.Despite the advantage of creating schedules with

auctions that fit naturally into a distributed architecture,the auction-based method is not well accepted in industrybecause of the unpredictability of the system performance,poor quality of schedules generated, and no theoreticalbasis for computing the bids and bid evaluation [7]. Thus, itis necessary to synchronize local planning and schedulingsolutions to attain a global optimal solution. Rational toolsare required to optimize globally rather than locally overthe extended enterprise. Besides, some recent studies showthat an integrated approach substantially outperforms thedecoupled one [15].

4. Concluding remarks and future work

This paper has attempted to analyze some specificproblems that arise at the extended enterprise as regardsplanning and scheduling decisions in SMEs and to identifythe most suitable techniques to tackle them. A case study ispresented, which describes a multi-site manufacturing firm,where its plants are geographically separated, but at the sametime located very close to each other. It is necessary tobroaden the scope of this case study to include manufacturingnetworks owned by different firms. In such a case, theplanning and scheduling issues will have to face the challengeof coordinating systems that do not fully share all the relevantinformation between each other. Agent-based and auction-based systems can be considered as some of the mostpromising techniques for implementing collaborative plan-ning and scheduling systems in the supply chain, althoughthey have not been able to provide a good enough globaloptimal solution that is well accepted in industry.Other important challenges are the definition of the

interactions and development of negotiation protocolsbetween the different echelons of the manufacturingnetwork at the planning and scheduling level. In anextended manufacturing network it is more difficult tomanage all the interactions that are necessary to ensurethat disruptions and related changes in one plant are takeninto account by other plants avoiding unnecessary waitsand inventory. This issue has not been sufficientlyaddressed.Our future work includes the development of generic

software modules for different industrial applicationsincluding the above-mentioned case study. More casestudies will be analyzed and included in the system to copewith a wide range of software applications.

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