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Resource Allocation Problems in Supply Chains PDF

197 Pages·2015·2.798 MB·English
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Resource Allocation Problems in Supply Chains Thispageintentionallyleftblank Resource Allocation Problems in Supply Chains By K. Ganesh McKinsey & Company, Inc., Chennai, India R. A. Malairajan Anna University, Tuticorin, India Sanjay Mohapatra Xavier Institute of Management, Mumbai, India M. Punniyamoorthy National Institute of Technology, Tiruchirappalli, India UnitedKingdom(cid:1)NorthAmerica(cid:1)Japan India(cid:1)Malaysia(cid:1)China EmeraldGroupPublishingLimited HowardHouse,WagonLane,BingleyBD161WA,UK Firstedition2015 Copyrightr2015EmeraldGroupPublishingLimited Reprintsandpermissionsservice Contact:[email protected] Nopartofthisbookmaybereproduced,storedinaretrievalsystem, transmittedinanyformorbyanymeanselectronic,mechanical, photocopying,recordingorotherwisewithouteitherthepriorwritten permissionofthepublisheroralicencepermittingrestrictedcopyingissued intheUKbyTheCopyrightLicensingAgencyandintheUSAbyThe CopyrightClearanceCenter.Anyopinionsexpressedinthechaptersare thoseoftheauthors.WhilstEmeraldmakeseveryefforttoensurethequality andaccuracyofitscontent,Emeraldmakesnorepresentationimpliedor otherwise,astothechapters’suitabilityandapplicationanddisclaimsany warranties,expressorimplied,totheiruse. BritishLibraryCataloguinginPublicationData AcataloguerecordforthisbookisavailablefromtheBritishLibrary ISBN:978-1-78560-399-0 ISOQAR certified Management System, awarded to Emerald for adherence to Environmental standard ISO 14001:2004. Certificate Number 1985 ISO 14001 Abstract R esourceAllocation(RA)involvesthedistributionandutiliza- tion of available resources in the system. Because resource availability is usually scarce and expensive, it becomes importanttofindoptimalsolutionstosuchproblems.Thus,RApro- blems representan importantclass ofproblemsfacedbymathemati- cal programmers. Conventionally, such RA problems have been modeled and solved for allocation in single-echelon Supply Chain (SC), single-objective allocation, and allocation with certainty of static input data, single-performance measure driven allocation, dis- integrated allocation and routing both in strategic and operational levels. Such models that consider the above assumptions/constraints are nominal models and their solutions are denoted nominal solutions. However, in practice, these assumptions are rarely, if ever, true, which raises questions regarding the practicability and validity ofthe problemsandsolutions obtained under these assump- tions. The allocation problems focusing bi- or multiple objectives, dynamic allocation bases on dynamic input data and constraints, multiple performance driven allocation and integrated allocation and routing context are complex combinatorial problems which demand high computational time and effort for deriving compro- mised near-optimal/optimal solutions. In this research, we study RA problems involving flow of resources over a typically, large-scale multi-echelonSCnetworkinanoptimalmanner. This research focuses on development of models and heuristics for six new and complex sub-classes of RA problems in SC network focusing bi-objectives, dynamic input data, and multiple perfor- mance measures based allocation and integrated allocation and routing with complex constraints. This study considers six set of variants of the RA problems normally encountered in practice but have not been given attention to hitherto. These variants of the clas- sical RA are complex and pertaining to both manufacturing and service industry. RA variant in bi-objective capacitated SC network, RA variant in bi-objective bound driven capacitated SC network, RA variant in multiple measures driven capacitated multi echelon SCnetwork,RAvariantinintegrateddecisionandupperbounddri- ven capacitated multi echelon SC network, RA variant in integrated decision and time driven capacitated multi echelon SC network, RA v vi ABSTRACT variant in integrated decision, bound and time driven capacitated multi echelon SC network are the new variants proposed in this research. These variants have some applications that are of special interest, including those that arise in the areas of warehousing, transportation, logistics, and distribution. These application domains have important economic value, and high importance is attachedtoachieveefficientsolutions. The Non-deterministic Polynomial (NP)-hardness of these pro- blems mandates the use of heuristics/meta-heuristics as solution methodology to solve these complex variants. Mathematical pro- gramming model, genetic algorithms, simulated annealing, simula- tion modeling, and decision-making models are used as solution methodologiestoaddressthesevariants.Thesolutionmethodologies are designed as unified methodology to solve the original or base variant of the proposed variants. The proposed unified solution methodologies are evaluated by comparing it with published results using standard, derived, and randomly generated data sets. In cases where benchmarks are not available, the published best results for the simpler versions of RA are used as substitutes for the lower bounds. The solution methodologies performed exceedingly well in the evaluations, recording better or equally good results in compari- sontotheexistingmethodologies. Keywords:Resourceallocationproblems;supplychain; mathematicalprogrammingmodel;heuristic;meta-heuristic; geneticalgorithms;simulatedannealing;simulatedmodeling Contents ListofTables xi ListofFigures xiii ListofSymbolsandAbbreviations xv AbouttheAuthors xxi SECTION1 Introduction 1 1.1. SupplyChainManagement 1 1.2. ResourceAllocationProblemsinSupplyChain 5 1.3. MotivationofResourceAllocationProblems 7 1.3.1. ResourceAllocationVariantinBi-Objective CapacitatedSupplyChainNetwork 7 1.3.2. ResourceAllocationVariantinBi-ObjectiveBound DrivenCapacitatedSupplyChainNetwork 8 1.3.3. ResourceAllocationVariantinMultipleMeasures DrivenCapacitatedMulti-EchelonSupplyChain Network 8 1.3.4. ResourceAllocationVariantinIntegratedDecisionand UpperBoundDrivenCapacitatedMulti-EchelonSupply ChainNetwork 9 1.3.5. ResourceAllocationVariantinIntegratedDecisionand TimeDrivenCapacitatedMulti-EchelonSupplyChain Network 9 1.3.6. ResourceAllocationVariantinIntegratedDecision, BoundandTimeDrivenCapacitatedMulti-Echelon SupplyChainNetwork 10 1.4. ScopeofthePresentStudy 10 SECTION2 Literature Review 13 2.1. ResourceAllocationProblem 13 2.2. ReviewoftheRAVariantsAddressedinCurrentResearch 14 2.2.1. Bi-ObjectiveGeneralizedAssignmentProblem 14 2.2.2. Multi-CommodityNetworkFlowProblem 15 2.2.3. MultipleMeasuresResourceAllocationProblem 21 vii viii CONTENTS 2.2.4. MixedCapacitatedArcRoutingProblem 24 2.2.5. EmployeeRoutingProblem 26 2.2.6. VehicleRoutingProblemwithBackhaulswithTime Windows 30 2.3. ObservationsandResearchGap 35 2.4. Summary 36 SECTION3 Bi-Objective Capacitated Supply Chain Network 37 3.1. Bi-ObjectiveResourceAllocationProblemwithVarying Capacity 37 3.2. SolutionMethodologytoSolveBORAPVC 39 3.2.1. MathematicalProgrammingModelfor BORAPVC 39 3.2.2. SimulatedAnnealingwithPopulationSizeInitialization throughNeighborhoodGenerationforGAPand BORAPVC 40 3.3. ComputationalExperimentsandResults 43 3.4. Conclusion 47 SECTION4 Bi-Objective Bound Driven Capacitated Supply Chain Network 49 4.1. Bi-ObjectiveResourceAllocationProblemwithBoundand VaryingCapacity 49 4.2. SolutionMethodologytoSolveIRARPUB 54 4.2.1. RecursiveFunctionInherentGeneticAlgorithm (REFING)forMCNFandBORAPBVC 54 4.3. ComputationalExperimentsandResults 58 4.3.1. PerformanceofSolutionMethodology 58 4.4. CaseStudyDemonstration 59 4.4.1. ProblemIdentificationandDiscussion 62 4.4.2. FormulationoftheProblem 66 4.4.3. ModelTesting 68 4.4.4. AnalysisofResultsandDiscussion 72 4.4.5. ManagerialImplications 72 4.4.6. SummaryforCaseStudy 72 4.5. Conclusion 73 SECTION5 Multiple Measures Driven Capacitated Multi-Echelon Supply Chain Network 75 5.1. MultipleMeasuresResourceAllocationProblemfor Multi-EchelonSupply 75 5.2. SolutionMethodologyforMMRAPMSC 76 Contents ix 5.2.1. SimulationModelingwithMultiplePerformances Measures(SIMMUM)forMMRAPMSC 76 5.2.2. ModelDescriptions 77 5.2.3. SIMMUMModelAssumptions 78 5.2.4. DecisionVariablesinSIMMUM 79 5.2.5. MultiplePerformanceMeasuresofMulti-Echelon SupplyChain 80 5.2.6. SIMMUMModelInitialization 81 5.2.7. SIMMUMModelExecution 81 5.2.8. OutputofSIMMUMModel 82 5.2.9. SIMMUMModelImplementation 85 5.3. SimulationModelExperimentationsandResults 86 5.4. CaseStudyforInventoryandPurchasingPolicy 90 5.4.1. ProcurementPolicyforall“A”ClassItems 91 5.4.2. InventoryPolicyforall“A”ClassItems 92 5.4.3. ProcurementandInventoryPolicyforall“B”“C” ClassItems 93 5.5. Conclusion 94 SECTION6 Integrated Decision and Upper Bound Driven Capacitated Multi-Echelon Supply Chain Network 97 6.1. IntegratedResourceAllocationandRoutingProblemwith UpperBound 97 6.1.1. Constraints 99 6.1.2. AssumptionsofIRARPUBProblem 99 6.2. SolutionMethodologytoSolveIRARPUB 100 6.2.1. Dijkstra’sAlgorithmandLocalSearchInherent GeneticAlgorithm(DIALING)forMCARPand IRARPUB 100 6.2.2. ParameterSettingsforDIALING 108 6.3. ComputationalExperimentsandResults 108 6.3.1. PerformanceofSolutionMethodology 109 6.4. CaseStudyforIRARPUB 111 6.5. Conclusion 113 SECTION7 Integrated Decision and Time Driven Capacitated Multi-Echelon Supply Chain Network 115 7.1. IntegratedResourceAllocationandRoutingProblemwith TimeWindow 115 7.2. SolutionMethodologytoSolveIRARPTW 116 7.2.1. ClusteringInherentGeneticAlgorithm(CLING)for VRPTWandIRARPTW 117

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