The Role of Discrete Event Simulation Techniques in Finite Capacity Scheduling

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The Role of Discrete Event Simulation Techniques in Finite Capacity Scheduling

Author(s): R. Roy and S. E. MeikleReviewed work(s):Source: The Journal of the Operational Research Society, Vol. 46, No. 11 (Nov., 1995), pp.1310-1321Published by: Palgrave Macmillan Journalson behalf of the Operational Research SocietyStable URL: http://www.jstor.org/stable/2584566.

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(e.g. precedence relationships,resource requirementsfor operations, capacityrestrictions).The objectives of scheduling, however, are not explicitly expressed but, instead, heuristicprocedures/rulesare incorporated n the model to derive 'good' acceptableschedules. OPTwas the first such tool to be developed and more recently many differentapproachesusingconstraint directed search techniques have been suggested13.For other researchers andpractitioners, the accurate, simple to understand, and computationallyefficient modellingcapabilityoffered by Discrete Event Simulation(DES) has made it an attractivechoice1417and a small but growingnumberof commercialsystemsare now available(e.g. PROVISA18andFACTOR19).The performanceof such techniques in meeting business goals very much depends on theheuristics used. However, except in the case of OPT, which is based on a well-definedphilosophyand principles,developmentof finite capacityschedulershas rarely been accompa-nied by a detailedexaminationof how they can serve the businessprocess. The objectives canonly be implied from the featurescontainedin the systems and their recommendeduse, orare left to the discretionof the user with the systems only acting as a modellingtool. The lackof an underlying schedulingphilosophyis also apparentin the case of existing DES-basedsystems. Unlike OPT, which is prescriptive n its approachto the development of schedules,DES-based systems have been consideredas merely modellingtools to assist in scheduling16.The danger,of course, is that while easingthe task of scheduling,the potentialbenefits to thebusinessmay not be fully realized, as currentworkingpracticesare reproduced n the modelandcontinued n the implementationof the system.This paparreviews the objectivesof finite capacityschedulingand examines the strengthsand limitationsof DES as a modelling tool. It also describes a DES-based finite capacityschedulingsystemthat has been developed,primarily or the pressworksndustry,and relateslessons learntduring ts implementation.

OBJECTIVESOF SCHEDULINGGoldratt and Fox1 described the fundamental objectives of any business as that ofsimultaneouslymaximizing ales, while minimizingnventory and other operating expenses. Itis a succinctstatement of the businessgoal, emphasizingboth competitiveperformanceandproductive use of resources. A scheduling system must contribute to this by helping topromote competitiveness hrough better deliveryperformance,and by improving he effectiveuse of resources to increase output of saleable goods while minimizing inventory andoperating expenses.The objectivesare not as contradictory s is sometimessuggested,at least

as long as they are considered ointly.Deliveryperformance

A schedulingsystemcan influence two areas of deliveryperformance-reliabilityand speedof delivery.Both factorshelp to reducesupplychaincosts and are increasinglybeing regardedas orderqualifiersby manycustomers.Delivery reliability s usuallymeasured either as a percentageof jobs delivered on time, oras an overall indicator of lateness of jobs (e.g. average lateness/tardiness).In a just-in-timebusinessenvironmentwith emphasison reliabilityof delivery,the formerwould seem to be amore appropriatemeasure. However, if it (or any other) is used as the sole performancemeasure,the result is variability n delivery performanceas shorter(easier) jobs are selectedfor processingin perference to longer, more urgent jobs. A schedulingsystem should bedesignedto take a long-termview of the businessas an on-going process, and avoid makingthe outputlook better in one period at the expense of creatingvariabilitybetween schedulingperiods, jobs and customers.Delivery speed is measured as the time between the receipt of an order (internal orexternal) and its delivery to the customer (i.e. the order fulfilment time). It is affected bymanyfactors(e.g. factorylayout, batch sizes, resourcelevels, informationsystems, lead-time


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Receipt of Order Deliveryto Customer(Internal or External) (Order Fulfilment)OverallLead-time (Delivery Speed)

Available for Release Ready for DeliveryPlanned ManufacturingLead-time

Transport Time

Actual ManufacturingLead-time

Actual CompletionShop-floor Lead-time

Actual ReleaseFIG. 1. Components of delivery speed.

variability in both; hence, finished goods inventory is directly related to planned manufactur-ing lead-time and the ability of the manufacturing system to meet its commitment consistentlywithin that period. Similarly, as discussed in the previous section, inventory of materialspurchased against specific jobs is also directly related to such lead-time, and it is notparticularly necessary to use a separate performance measure to judge the effectiveness of ascheduling system in minimizing inventory. In fact, delivery speed and stability in the supplychain relationship are two key elements in competitiveness and cost reduction22.

ThroughputPotential throughput of a production system is governed by the capacity of its bottleneckresources. Hence, a scheduling system needs to ensure that the capacity of such resources iseffectively utilized and that the resources are not starved of work as long as there is realdemand for their output. The objective is wholly consistent with maintaining the highest

possible level of delivery performance since the latter suffers if the production rate falls belowthe demand rate; the requirement is to ensure that production output matches demand overthe planned manufacturing lead-time.In order to maximize throughput (or at least match demand rate without excessiveovertime) a scheduling system must maintain the necessary WIP levels on the shop-floor sothat bottleneck resources can operate efficiently. Similarly, rules/algorithms used to schedulemovement of jobs through the shop-floor must also try and avoid starvation of such resources,and ensure a balanced flow of production23.Production flow is aided by small transfer batch sizes (pallet loads), short set-up times andcellular layouts with short travel times, which often have a much greater influence than ascheduling system. Nevertheless, rules and algorithms designed to improve production floware important if they help to maximize throughput with minimum inventory. The issuebecomes more complicated if it becomes necessary to batch specific jobs or sequence them ina particular order to maximize utilization of critical resources (e.g. through reduced set-uptimes).Any rule or algorithm designed to improve throughput would, inevitably, not prioritize jobsby their due dates and, hence, lead to move variable achievement of lead-times; plannedmanufacturing lead-time and, therefore, inventory levels would need to be increased toaccommodate such variability. However, since delivery performance also suffers if production


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rate falls below that for demand, appropriatemethods for meetingboth objectivesneed to beinvestigated. Simple flow-orientedheuristicpriorityrules (e.g. based on Shortest ProcessingTime), which lead to high variability n lead-timeswithoutnecessarily ncreasing hroughputare not the answer;methods that more directlyaddressthe utilizationof bottleneck resourcesare required.Evidence of researchon the measurementof throughput s rathersparsein the schedulingliterature.A common measureused in industry s standardhours workedby a particular lassof resource(labouror machines). However, it is a measureof input, rather than output, andencourages obs to be pushed onto the shop-floorand inappropriateprioritiesto be assignedto operations, leadingto unnecessarywork-in-progress.A better measurewould be based onoutput of completed jobs. Goldratt1'suggestedthe use of throughputaccountingprinciples,and further research is needed to investigate how this could be used to measure actualthroughputagainstthe true capabilityof a shop.

Operating xpensesIn the discreteparts manufacturingndustry,the principaleffect of a schedulingsystemonoperating expenses (other than inventory)is usuallythat of overtime cost. Overtimeresultswheneverthe demand that needs to be met in a period exceeds production capacity and, asbefore, the objective of a scheduling system must be to maintain planned manufacturinglead-timeand inventoryat a level necessaryto smooth demand to a reasonable extent andmaximize he utilizationof the productivecapacityof the shop (unless capacity s in surplus).Otherexamplesof relevantoperatingexpensesinclude the degradationor loss of perishableraw material that may result from slow movement through a plant, loss of paint qualitycaused by coloursequencesand changeovers n a paint shop, and the severe quality problemsthat can result in the steel industryif a particularsequence of coil widths is not followed.

Where such costs are dependent upon the sequence in which jobs are processedon one ormore workcentres,the objective of minimizingthe cost is at conflict with that of deliveryperformance. Sequencingconstraintsof this type are most prevalentin the process industry,but many engineeringcompanieshave one or two workcentreswhere such considerationsmayapply. OPT does not deal with sequence dependentcosts, excepton capacity.

APPROACHESTO MODELLINGThe basic problem of scheduling can be described as that of allocation of availableresources to a numberof jobs over a given period of time in order to meet certain businessobjectives. A scheduling system develops a production plan based on a model of theoperation of the manufacturing ystem. In the case of logical modelling techniques, threebasic approaches can be identified, depending on whether the model that drives theschedulingmechanisms resource, job or time based.

Resource-basedmodelIn a resource-basedmodel all machines (or workcentres)are first ordered according o theseverity of theircapacity constraintsor schedulingrestrictionsplaced on them. Each resourceis then scheduled in turn with the operations that require its usage; the sequence is thendetermined according o some defined criterion, e.g. to maximize the resource's throughputor to satisfy quality objectives (usuallywith the urgency of the jobs as a secondarycriterion).As each resource is scheduled it places temporalconstraintson the schedules of subsequentresourcesto be considered;this inevitably eads to conflicts in the schedule, which the systemthen needs to resolve. A resource-basedmodelling tool tends to be complex with a highcomputationalburden. For the system to be computationallymanageable, a resource-basedmodel is best used in the case of a shop where workcentres with significant capacity or


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sequence constraints are very small in number. An example of a resource-basedmodel isOPT24.Job-basedmodel

In a job-(or order-) based model the first step is to order the jobs according o some userdefined rule. Each job is then takenin turn and all its associatedoperationsare scheduled onthe requiredresources(usually workcentres).The result is a valid schedule,but likely to be ofa poor quality. It may then be improvedthroughthe iterative applicationof some algorithmor heuristicrules or, as is more often the case, with the direct interventionof expert planningstaff. The use of Gantt charts as a manualor electronicmodelling technique is an example ofa job-based approach25.Time-basedmodel

In a time-based model the system uses an incrementalapproach o move forward n time,and at each increment idle resources are considered for allocation of jobs waiting to beprocessed. Heuristic rules are used to prioritize jobs and make choices on allocation;utilizationof bottleneckresources can be affectedby the heuristic rules that are chosen, butnot directlyaddressedby the system, as in the case of resource-basedmodels. The variabletime increment(i.e. next event) method used in DiscreteEvent Simulation DES) makes it acomputationally fficienttime-basedapproach or developingmodels of a system.Recent research on finite capacity schedulingwith logical modelling has tended to focus ontwo broad methods-the use of Al techniqueson a hybrid formulationcombiningresourcesand job based approaches(e.g. OPIS26), and the use of DES as a time-basedtechnique. Inthe case of the former,the entire operationof the manufacturing ystem is modelled as a setof constraints (e.g. precedence, capacity, due-dates) and appropriateconstraint-directedsearch techniques (including constraint relaxation) are then used to solve the resultingproblem;heuristic rules are employedto define the precedence order in which jobs and/orresources are consideredin satisfyingthe constraints.DES, in contrast,develops a scheduleincrementally,with one element of it decidedat each time update. Precedence constraintsarerepresented as process routes, and due dates are only considered in the heuristic rulesemployed in the selection of jobs and resources at each time update. DES also deals withsatisfaction of constraints (mainly capacity), but considers at each time update only theconstraintsprevalentat that time.

DISCRETEEVENT SIMULATIONDiscrete Event Simulationhas three importantcharacteristics: t has a distinctmodellingtechnique, it is an experimental tool for analysis of systems and it has the ability toincorporate tochasticbehaviour.

Modelling echniqueAs a modelling technique used in scheduling, DES has four important advantages.Techniquesthat treat schedulingas a pure constraintsatisficationproblemtend to produceunstable schedules, i.e. small changes in parametersof the manufacturing ystem (or anyheuristicsused within the solution) can very substantiallyalter the characterof the schedulethat is generated.In contrast, DES with its incrementalapproachto schedule developmentproduces relatively more stable schedules. Techniquesthat involve some form of resource-based modelling also tend to be complex with a high computationalburden and, to keep theproblem tractable,often many detailed characteristics f the manufacturingystem may needto be omitted from the model. (Unlike in the case of mathematicalmodels, computational


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efficiency actuallydecreasesvery rapidlyfor highly constrainedsystems, since the need forbacktrackingis greater during the search process). DES, on the other hand, is wellestablished as an efficient modelling tool, and enables very accuratemodels to be executedrelativelyefficiently;the level of accuracyof the manufacturingystemmodel assumesgreaterimportance in a lean manufacturing nvironment,since the effect of interactionsbetweencomponentsof a system becomes more pronouncedwith reduction n lead-timeand WIP. Anappropriately onstructedmodel can also be first used in its more traditionalrole of analysingthe design of the manufacturing ystem before it is implementedas an operationalplanningtool. Finally, a Visual Interactive Simulation(VIS) system makes the modellingmechanismverytransparent nd, hence, more readily acceptable o productionpersonnel.Discrete event simulation,however,has two important imitationsthat can affect its use inscheduling. Incrementalscheduling decisions at each time interval have to be made usingheuristicswithout a proper appreciation of their effect on the rest of the schedule. Suchmyopic decisions may lead to an unacceptableor poor schedule as objectives of criticalresourcesare not met. Starvationof bottleneckresources can usually be avoided by ensuringthat a sufficientamount of work is maintainedon the shop-floor;theoretically, a resource-based approachmay achieve this with lower WIP, but the greater accuracyof a DES modelwill often compensate. It does, however, become a problem when a numberof resources n anetwork of job routes have individual sequencing constraints/objectives hat need to befulfilled. There is a need to investigatemethods for providinga DES model with intermediategoals for its incrementaldecisionmakingprocess.Similarly,a lack of a hierarchicalmodellingtechnique in discrete event simulation affectsthe way it is used. DES works on the basis of process routes, and does not use a Bill ofMaterialsor model of the structureof the enterprise. Its use is limited, at least in the case oflarge and complex enterprises, o that of a shop/cellplanning ool. It is normallyused with anMRP II system, which provides the model of the supply chain within the enterpriseandschedules delivery parts between its different units. Lack of an integrated frameworklengthens the planning cycle for the supply chain, and slows down the evaluation andtransmissionof changes in local schedules. A model of the enterprise with links to the cellmodels can help in the design of a more efficient chain, and as a planningtool for bettercoordination between cells while still retaininglocal responsibilityfor schedulingof partswithin them. Often, a coarse model of the enterprisewill be sufficient,with its constituentsexplodedinto DES models for local use but, as the enterprisegets leaner, the models of thecritical cells may also need to be directlyused by the higher level planningsystem. Such atool can only by truly effective in a supply chain partnershipenvironment based on TotalQualityManagement TQM), and practicalmethodswill have to be developedfor its use as atool for cooperationbetween the cells.

Experimental toolDiscrete event simulation s a well-establishedexperimental ool in the strategic analysisofmanufacturing ystems. An advantageof its use in production planningstems from the factthat a DES model can be used in such analysis prior to its implementationas a schedulingtool. It should be used initiallyto help in any necessaryredesignof the manufacturingystem,and to establish the required level of WIP on the shop-floor and lead-times that can beachieved. As a scheduling tool, its use, which is often advocatedin the literature16,s forexperimentationwith differentpriorityrules. This only prioritizesone job at the expense ofanother, leads to increased variability in lead-time, and often does not increase actualthroughput. A second approachto experimentationsometimes suggested is to provide theplannerwith a visual interactiveplanning board25;his is feasible only if the number of 'live'jobs and key resourcesthat need to be consideredare fairlylimited. In most cases, however,experimentationshould mainly concentrate on findingsome ways of eliminating ateness ofjobs, if any, by resolving capacity constraints;possible solutionsare overtime, subcontractingjobs/operations,and agreeing with the customeron split deliveryor a revised deliverydate.By concentratingon a limitednumber of options, the job of the planner s made manageable.


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The firstuse of the tool is, hence, in short-termcapacityplanning; n addition,a 'work-to' istand a detailed activity timing plan are producedto help monitorprogressof jobs and devisecorrectiveactionsin case of disruption shop-floorcontrol).

Stochastic system behaviourDiscrete event simulation has always been regardedas a tool for analysisof stochasticsystems, but probabilisticdistributions have no statistical validity in the short planninghorizonsnormallyused in scheduling.The use of 'time buffers'is, instead, recommended oproduce more robustachievableschedulesthat have taken the effect of randomdisturbancesinto account. The concept of time buffersis well established n schedulingand a number oftypes can be used. Capacity protection for critical resources and uncertaintyabout processdurationcan be catered for throughsuitable allowanceson operations/set-up imes; the use oftravel times reducesthe effect of late completionof one operationon the rest of the schedule.The aim, however, should be progressively o reduce the levels of uncertainty n the systemand adjustthe time buffersaccordingly.A model used for schedulingshouldalso incorporateany known disturbances o the operation of the manufacturing ystem (e.g. known break-downs, unavailability f material, etc). The planning ntervalshould be kept short to improveboth deliveryspeed and robustnessof the schedule. Not everydisruptionwill have an adverseeffect on delivery promises(e.g. breakdownof non-bottleneckmachines)but, when it seemslikely, the usualoptionsare againovertime,sub-contractingwork, split delivery,etc. Methodsfor schedulerepair,however, still remain an importantarea that needs furtherresearch;theobjectivemustbe to maintainsupplychainreliability.

A PLANNINGAND CONTROLSYSTEMA DES-based schedulingsystem, PCSS (Planning and Control SimulationSystem), hasbeen developedincorporating ome of the ideas presentedabove. It was developed primarilyfor use in the pressworksindustry and has been validated in three companies. A moredetailed presentationof PCSS's scope and operation can be found in Roy et al.27, but anoverview is providedhere.The core element of the system is a data-drivengenericsimulationsystem(developedusingthe VIS toolkit See-Why28), whichcan model in detail the operationof press shops, or otheroperating units engaged in similar made-to-order or batch production. Examples of the

featuresthat can be modelled include multipleshift patternsand labourtypes, sub-contractoperations,programmedevents such as planned maintenanceand holidays, alternateroutes,complexflow patternsfoundin pressshops such as 'follow-on'and press-lines, and the abilityto model unavailability f tools, materialor other productionresources.In addition to short-termscheduling,PCSS is also suitablefor more conventional use as adecision supporttool for strategicdecision makingand general improvements o productionsystems. Stochastic effects-such as random machine breakdowns-can be introduced toprovide representationof capacity losses in a way that is appropriateand necessary forstrategicanalysis,but not for short-termproductionscheduling.Individualmodels are createdwithin the systemthrougheight data files. These files define,respectively, the details of the shop and machines, labour, other production resources,manufacturing perations, orders, initial WIP, tooling information,and a calender of eventssuch as holidays.Data files may be createdthrough the use of a front-enddata entrysystem,but in practicethey are most convenientlycreated by downloading rom an existing companymanufacturingatabasesuchas MRP II.Release of jobs onto the shop-floor s controlled in the model throughthe use of a plannedmanufacturingead-time and an input control mechanism.If only the former is used, therelease mechanismbehaves similarlyto an MRP system, with jobs made available to themodel by a pre-determined ntervalbefore their due dates; the intevrvalorresponds o a job


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IMPLEMENTATIONExtensivetrials of PCSS have been carried out in three companies,and it is in the processof being fully implemented n the press shop of a medium-sizedcompanyin the automotivesector. The press shop has three sources of demand-parts for current vehicle production,spare parts and a small but increasing amount of sub-contractwork. The process ofimplementingPCSS has been a long and arduous ask, primarily or two reasons-lack of dataintegrity in the company's existing planning system, and the difficulty in adaptingto theculturalchanges needed. The period of implementationcan be divided up into three (oftenoverlapping)phases, which are probablycommonin any implementationof a finite capacityschedulingsystem. This has highlighted he need for the extensivepreparation hat is requiredbefore such a task in undertaken.The first phase was concernedostensiblywith validation of the system but, in reality, wasmuch more involved with data validation. The company already had an established MRPsystem, and much of the data requiredfor PCSS was taken directly from it. Although the

MRP system had been in existence for a long time, trial implementationof PCSS soonrevealed the poor quality of the data on which planningdecisions were being made. Thisbecame evident from major differences between predicted shop performance and theexpectationsof the shop-floor supervision;e.g. in utilization of resources, identificationofbottlenecksand feasibilityof trialschedules.The problemswith the MRP data were, in manycases, known to the shop-floor,but effective communicationof them to senior managementwasnot achieved until all partiesviewed and discussed he simulationresults. Many months ofproductionengineers' time were then taken up in rectifying the MRP database, with theresultsfrom the simulationmodelplayinga crucialrole in this audit.In the second phase, a numberof strategicanalyseswere carriedout. Differentoptions forimprovingthe operation of the press shop were studied using PCSS. These included theintroductionof flexible working, prioritizing asks in a set-up time reductionprogrammeandtheir financial justification, assessing the advisabilityof accepting large amounts of sub-contractwork, and determiningabourrequirement.The value of a single model for strategicanalysisand operationalplanningwas amplydemonstratedduring his phase.The final phase was concernedwith gaining acceptanceof PCSS as a schedulingtool fromshop-floorpersonnel. Initially the system was championed by the ProductionEngineeringManager,but it was always recognizedthat its successwould dependon Productionacceptingownership.Weeklyschedulesdevelopedby the system(togetherwith Gantt chartrepresenta-tions) were regularly given to the press shop supervisorfor his comments. After somerequested modificationswere implemented,the schedules were accepted by him as achiev-able, but this did not alwayslead to the suggestedsequence of workbeing followed. This waspartly due to corruptionof the schedule, often brought about by inaccuracies n data onfinishedgoods stock, but there was also a reluctance o have a sequenceof work dictatedby aschedulingsystem;to do so would require completelychangingcurrentworkingpracticesinwhich the shop-floor decided on their own job priorities (often based on convenience) inmeetingthe weekly MRPtargets.This highlights he need for a finite capacityscheduling oolproducinga detailed timing plan to be owned by the shop-floorfor local planning,and not bya remote department.As for PCSS, this is now gaining n acceptance(helped by the arrivalofa new press shop manager).Both the press shop managerand key schedulirqgtaff have beentrainedin its use; the accuracyof data has now reachedan acceptable evel, and plans havebeen drawnup for its full implementation.It is expectedto be used in four ways-to providea detailed work plan for the shop-floor, to help in a progressreview at daily productionmeetings, to identifyidle periods when preventativemaintenancecan be carried out and toidentifyresources or whichit is appropriateo seek sub-contractwork.

CONCLUSIONSDiscrete event simulationbased scheduling ools have been gaining acceptanceas importantaids to CAPM. However, for them to be truly effective they must be more than just


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modelling tools and should contribute significantly to improvement in business performance.Their implementation must be based on sound principles that promote stability and efficiencywithin manufacturing, and this paper has suggested possible approaches that may be adopted.In the discrete engineering industry, the subject of this work, delivery speed and reliabilitymust be the key goals, which not only enhance competitiveness but also reduce supply chaincost.Implementation of a finite capacity scheduling system is a long and arduous task, andshould only be undertaken after a thorough examination of the manufacturing system and therobustness of the supporting information system; however, a DES-based tool naturally lendsitself to that process. There is also the need to pay attention to the changes in workingpractices that are needed. For these systems to be effective in bringing real businessimprovement, short lead-times, low inventory and schedule adherence must become part ofthe company's culture.As a modelling tool, DES has some advantages but also limitations, and there is a need forfurther research on the development of more versatile tools that can have comprehensiveapplications in finite capacity scheduling.

REFERENCES1. J. SCHENGILLI 1986) Optimal scheduling. In Proceedings of the 2nd International Conference on Simulation inManufacturing. (J. E. LENZ, Ed.) pp. 139-145. IFS, Chicago, USA.2. H. H. ADELSBERGERand J. J. KANET (1991) The Leitstand-a new tool for computer-integrated manufacturing.J. Prod. Invent. Mgmt 32(1), 43-47.3. P. R. PHILIPOOMand T. D. FRY (1992) Capacity-based order review/release strategies to improve manufacturingperformance. Int. J. Prod. Res. 30, 2559-2572.4. E. H. BOWMAN(1959) The schedule-sequencing problem. Opns Res. 7, 621-624.5. E. IGNAL and L. SCHRAGE (1965) Application of the branch and bound technique to some flow-shop scheduling

problems. Opns Res. 13, 400-412.6. Z. A. LOMNICKI(1965) A 'branch-and-bound' algorithm for the exact solution of the three-machine schedulingproblem. Opns Res. Q. 16, 89-100.7. J. SCHULTE and B. BECKER (1992) Production scheduling using genetic algorithms. In Proceedings IFACConference, INCOM'92. (M. B. ZAREMBA, Ed.) pp. 527-533. International Federation of Automatic Control,Toronto.8. W. S. GERE Jr. (1966) Heuristics in job shop scheduling. Mgmt Sci. 13, 167-189.9. E. M. DAR-EL and R. A. WYSK (1982) Job shop scheduling-a systematic approach. J. Mftg Syst. 1, 77-88.10. A. D. NEELY and M. D. BYRNE (1992) A simulation study of bottleneck scheduling. Int. J. Prod. Economics 24,187-192.11. E. M. GOLDRATTand R. E. Fox (1986) The Race. North River Press, Croton-on-Hudson, New York.12. A. A. B. PRITSKER,L. W. MILLER and R. J. ZINKL (1971) Sequencing n products involving m independent jobson m machines. AIIE Trans. 3, 49-60.13. M. S. Fox (1987) Constraint-Directed Search: A Case Study of Job-Shop Scheduling. Pitman, London.14. F. BONSIGNORIO,M. BOTiTERO, . BUSATiTInd R. FERRARI (1988) A simulation tool integrated in a short termplanning system. In Proceedings of the 4th International Conference on Simulation in Manufacturing. (J. BROWNEand K. RATHMILL,Eds) pp. 107-115. IFS/Spinger-Verlag, London, UK.15. J. DUGAN and J. BROWNE (1988) An AI based simulation for production activity control systems. In Proceedingsof the 4th International Conference on Simulation in Manufacturing. (J. BROWNE and K. RATHMILL, Eds)pp. 177-194. IFS/Spinger-Verlag, London, UK.16. H. GRANT (1986) Production scheduling using simulation technology. In Proceedings of the 2nd InternationalConference on Simulation in Manufacturing. (J. E. LENZ, Ed.) pp. 129-138. IFS, Chicago, USA.17. E. K. WICHMANN1990) A methodology for development of simulation based production schedule generationsystems. In Proceedings of the 1990 Winter Simulation Conference. (B. OSMAN, R. P. SADOWSKI,R. E. NANCE,Eds) pp. 619-624. IEEE, New York.18. AT&T ISTEL LTD (1993) Provisa User Manual. AT&T Istel Ltd Redditch, Worcs, UK.19. D. MAcFARLAND and M. E. GRANT (1990) Tutorial: scheduling manufacturing systems with Factor. InProceedings of the 1990 Winter Simulation Conference. (B. OSMAN, R. P. SADOWSKIand R. E. NANCE, Eds)

pp. 146-148. IEEE, New York.20. J. D. C. LITTLE1961) A proof for the queueing formula: L = AW. Opns Res. 9, 383-387.21. S. S. PANWALKERand W. ISKANDER(1977) A survey of scheduling rules. Opns Res. 25, 45-61.22. T. HILL (1985) Manufacturing Strategy. Macmillan Education, Basingstoke, UK.23. E. M. GOLDRATiTnd J. Cox (1984) The Goal, Excellence in Manufacturing. North River Press, Croton-on-Hudson, New York.24. T. E. VOLLMANN(1986) OPT as an Enhancement to MRP II. J. Prod. Invent. Mgmt 27(2), 38-47.25. L. J. WALKER and J. D. WOOLVEN (1991) Development and use of a visual interactive planning board withinAlcan Aluminium. Eur. J. Opl Res. 54, 299-305.26. P. S. Ow and S. F. SMITH (1988) Viewing scheduling as an opportunistic problem solving process. Ann. OpnsRes. 12, 85-108.


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27. R. Roy, P. A. MCCARTHY nd S. E. MEIKLE1992) Controlling press shops using simulation. In Sheet Metal1992-Proceedings of the International Conference 7-9 April 1992. (B. SHIRVANI and D. R. H. BAGGS, Eds)pp. 261-270. IOP Publishing, Birmingham, UK.28. E. FIDDY, J. G. BRIGHT nd R. D. HURRION (1981) SEE WHY: interactive simulation on the screen. Presentedat the Conference of the Institute of Mechanical Engineers, December 1981, London, Paper No. C293/81.29. S. A. MELNYK nd G. L. RAGATZ 1989) Order review/release: research issues and perspectives. Int. J. Prod.Res. 27, 1081-1096.

Received October 1994; accepted February 1995 after one revision

The Role of Discrete Event Simulation Techniques in Finite Capacity Scheduling

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