This book deals with stochastic combinatorial optimization problems in supply chain disruption management, with a particular focus on management of disrupted flows in customer-driven supply chains. The problems are modeled using a scenario based stochastic mixed integer programming to address riskneutral, risk-averse and mean-risk decision-making in the presence of supply chain disruption risks. The book focuses on integrated disruption mitigation and recovery decision-making and innovative, computationally efficient multi-portfolio approach to supply chain disruption management, e.g., selection of primary and recovery supply portfolios, demand portfolios, capacity portfolios, etc. Numerous computational examples throughout the book, modeled in part on realworld supply chain disruption management problems, illustrate the material presented and provide managerial insights. Many propositions formulated in the book lead to a deep understanding of the properties of developed stochastic mixed integer programs and optimal solutions. In the computational examples, the proposed mathematical programming models are solved using an advanced algebraic modeling language such as AMPL and CPLEX, GUROBI and XPRESS solvers. The knowledge and tools provided in the book allow the reader to model and solve supply chain disruption management problems using commercially available software for mixed integer programming. Using the end-of chapter problems and exercises, the monograph can also be used as a textbook for an advanced course in supply chain risk management. After an introductory chapter, the book is then divided into six main parts. Part I addresses selection of a supply portfolio; Part II considers integrated selection of supply portfolio and scheduling; Part III looks at integrated, equitably efficient selection of supply portfolio and scheduling; Part IV examines integrated selection of primary and recovery supply and demand portfolios and production and inventory scheduling, Part V deals with selection of resilient supply portfolio in multitier supply chain networks; and Part VI addresses selection of cybersecurity safequards portfolio for disruption management of information flows in supply chains.

Flexibleassemblysystems(FASs)haveemergedasaresultofthedevelop- mentsinmanufacturingandcomputertechnology. Currentmarketrequire- mentscharacterizedby -increasingnumberofdifferenttypesandversionsofproducts, -smallerbatchsizes,and -shorterlife-timeofproducts, stronglydeterminethecompetitivenessinproductionassemblyandaddi- tionallycontributetothedevelopmentofflexibleautomatedassembly. For example,attheendof1986[33]40%ofJapaneserobotswerespecialized inassemblyascomparedwithonly10%ofEuropeanrobots. Theremain- ing90%wereusedinwelding,painting,andhandling. Theintroductionof flexibleautomatedassemblytohigh-techsectorswhereassemblycostsare criticalistheaimofmajorEuropeanprojectssuchasESPRITandBRITE programmesandtheFAMOS-EUREKAproject,e. g. ,[33,34]. Thebookdealswithproductionplanningandschedulinginflexibleassem- blysystems. ThereaderisfamiliarizedwiththeFASplanningandschedul- ingissuesforwhichvariousoperationsresearchmodellingandsolutionap- proachesarediscussed. Inparticular,applicationsofintegerprogrammingto theFASshort-termplanningandfastcombinatorialheuristicstotheFAS schedulingarediscussed. Thematerialinthebookhasbeendividedintosevenchapters. Chapter1presentstheoverallstructureandhardwarecomponentsand featuresofaflexibleassemblysystem. TheFASsclassificationisprovided andillustratedwithindustrialapplicationsofmechanicalpartassemblyand printedcircuitboard(PCB)assembly. Chapter2discussesmajorissuesinthedesign,planningandschedulingof flexibleassembly. BasicconfigurationsofFASsandmaterialflownetworksare presentedandvariousapproachestodesignforautomatedassemblyandto assemblyplanningarediscussed. TheFASproductionplanningandschedul- ingareconsideredwithinahierarchicalframeworkwithmachineloadingand assemblyroutingatanupperlevelandmachineandvehicleschedulingata lowerlevel. Finally,specificissuesinplanningandschedulingofPCBassem- blyarediscussed. VIII Preface InChapter3variousbi-objectiveintegerprogrammingmodelsandso- lutionapproachesarepresentedformachineloadingandassembly-routing inFASs. Aninteractiveprocedureisproposedforsimultaneousloadingand routingbasedonweightingapproachandalexicographicalgorithmisgiven forsequentialloadingandroutingwithalinearrelaxationloadingheuristic andanetworkflowroutingmodel. Numericalexamplesillustratepossible applicationsofthemodellingandsolutionapproachespresented. InChapter4thesequentialmodellingandsolutionapproachproposedin Chapter3hasbeenextendedforabicriterionmachineloadingandassembly routingwithsimultaneousassemblyplanselectioninageneralFASandina flexibleassemblyline. Numericalexamplesareprovidedtoillustratepossible applicationsoftheapproachproposed. Chapter5presentsmathematicalprogrammingformulationsforsimul- taneousloadingandschedulinginflexibleassemblycells. Theformulations areillustratedwithpracticalapplicationsinmechanicalpartassemblywith arobotassemblycellandinPCBassemblyonacomponentplacementma- chine. Chapter6isdevotedtoproductionschedulinginflexibleassemblylines whereseveralassemblystagesinseriesareeitherseparatedbyfiniteinter- mediatebuffersortherearenobuffersbetweenthestages,andeachstage consistsofoneormoreidenticalparallelmachines. Fastpush-typeschedul- ingheuristicsareproposedforthelinewithlimitedintermediatebuffersor thelinewithnoin-processbuffers. Foracomparison,apull-typeschedul- ingstrategyisillustratedwithsomerecentresultsfortheJust-In-Timeand multilevelschedulingofflexibleassemblylines. Numericalexamplesprovide thereaderwithpossibleapplicationsofthevariousmodellingandsolution approachespresented. InChapter7simultaneousschedulingofassemblystationsandautomated guidedvehiclesisdiscussedforageneralFASandtwodifferentsolutionap- proachesarepresented:(i)amulti-levelapproach,inwhichfirstmachine loadingandassemblyroutingproblemissolvedandthen,giventaskassign- mentsandassemblyroutesselected,detailedmachineandvehicleschedules aredetermined;(ii)asingle-levelapproach,inwhichmachineandvehicle schedulesaredirectlydeterminedwithnoinitialloadingandroutingdeci- mentscharacterizedby -increasingnumberofdifferenttypesandversionsofproducts, -smallerbatchsizes,and -shorterlife-timeofproducts, stronglydeterminethecompetitivenessinproductionassemblyandaddi- tionallycontributetothedevelopmentofflexibleautomatedassembly. For example,attheendof1986[33]40%ofJapaneserobotswerespecialized inassemblyascomparedwithonly10%ofEuropeanrobots. Theremain- ing90%wereusedinwelding,painting,andhandling. Theintroductionof flexibleautomatedassemblytohigh-techsectorswhereassemblycostsare criticalistheaimofmajorEuropeanprojectssuchasESPRITandBRITE programmesandtheFAMOS-EUREKAproject,e. g. ,[33,34]. Thebookdealswithproductionplanningandschedulinginflexibleassem- blysystems. ThereaderisfamiliarizedwiththeFASplanningandschedul- ingissuesforwhichvariousoperationsresearchmodellingandsolutionap- proachesarediscussed. Inparticular,applicationsofintegerprogrammingto theFASshort-termplanningandfastcombinatorialheuristicstotheFAS schedulingarediscussed. Thematerialinthebookhasbeendividedintosevenchapters. Chapter1presentstheoverallstructureandhardwarecomponentsand featuresofaflexibleassemblysystem. TheFASsclassificationisprovided andillustratedwithindustrialapplicationsofmechanicalpartassemblyand printedcircuitboard(PCB)assembly. Chapter2discussesmajorissuesinthedesign,planningandschedulingof flexibleassembly. BasicconfigurationsofFASsandmaterialflownetworksare presentedandvariousapproachestodesignforautomatedassemblyandto assemblyplanningarediscussed. TheFASproductionplanningandschedul- ingareconsideredwithinahierarchicalframeworkwithmachineloadingand assemblyroutingatanupperlevelandmachineandvehicleschedulingata lowerlevel. Finally,specificissuesinplanningandschedulingofPCBassem- blyarediscussed. VIII Preface InChapter3variousbi-objectiveintegerprogrammingmodelsandso- lutionapproachesarepresentedformachineloadingandassembly-routing inFASs. Aninteractiveprocedureisproposedforsimultaneousloadingand routingbasedonweightingapproachandalexicographicalgorithmisgiven forsequentialloadingandroutingwithalinearrelaxationloadingheuristic andanetworkflowroutingmodel. Numericalexamplesillustratepossible applicationsofthemodellingandsolutionapproachespresented. InChapter4thesequentialmodellingandsolutionapproachproposedin Chapter3hasbeenextendedforabicriterionmachineloadingandassembly routingwithsimultaneousassemblyplanselectioninageneralFASandina flexibleassemblyline. Numericalexamplesareprovidedtoillustratepossible applicationsoftheapproachproposed. Chapter5presentsmathematicalprogrammingformulationsforsimul- taneousloadingandschedulinginflexibleassemblycells. Theformulations areillustratedwithpracticalapplicationsinmechanicalpartassemblywith arobotassemblycellandinPCBassemblyonacomponentplacementma- chine. Chapter6isdevotedtoproductionschedulinginflexibleassemblylines whereseveralassemblystagesinseriesareeitherseparatedbyfiniteinter- mediatebuffersortherearenobuffersbetweenthestages,andeachstage consistsofoneormoreidenticalparallelmachines. Fastpush-typeschedul- ingheuristicsareproposedforthelinewithlimitedintermediatebuffersor thelinewithnoin-processbuffers. Foracomparison,apull-typeschedul- ingstrategyisillustratedwithsomerecentresultsfortheJust-In-Timeand multilevelschedulingofflexibleassemblylines. Numericalexamplesprovide thereaderwithpossibleapplicationsofthevariousmodellingandsolution approachespresented. InChapter7simultaneousschedulingofassemblystationsandautomated guidedvehiclesisdiscussedforageneralFASandtwodifferentsolutionap- proachesarepresented:(i)amulti-levelapproach,inwhichfirstmachine loadingandassemblyroutingproblemissolvedandthen,giventaskassign- mentsandassemblyroutesselected,detailedmachineandvehicleschedules aredetermined;(ii)asingle-levelapproach,inwhichmachineandvehicle schedulesaredirectlydeterminedwithnoinitialloadingandroutingdeci- sionsrequired. Foreachapproachaschedulingalgorithmbasedondynamic complexdispatchingrulesisproposedandnumericalexamplesareprovided toillustrateandcomparethetwoschedulingapproaches. Thematerialpresentedinthebookisillustratedwithnumerousexamples, figuresandextensivetables. Thereaderisprovidedwithdetailedmathemat- icalmodelsoftheFASplanningandschedulingproblemsanddescriptions ofthesolutionalgorithmsproposed. Theirapplicationsareillustratedwith manynumericalexamplesandresultsofvariouscomputationalexperiments withthemodelsandalgorithmsarereported. Preface IX Thebookisaimedprimarilyatstudentsandprofessionalsinproduction andoperationsmanagement,industrialandsystemsengineering,andauto- matedmanufacturing. Thisbookbenefitedfromnumerousdiscussionswithmycolleagues. Pro- fessorAndreasDrexlandDr. RainerKolischfromtheChristian-Albrechts UniversityofKieldeservespecialthanksforthecarefulreadingofvarious partsofthemanuscriptandtheirvaluablecomments. ThebookhasbeenpreparedwithpartialsupportbyKBNresearchgrant #8TllF01513,AGHgrant#10. 200. 10,andTEMPUS-PHAREproject #S_JEP-09434-95. TadeuszSawik DepartmentofComputerIntegratedManufacturing FacultyofManagement UniversityofMiningandMetallurgy Krakow,Poland TableofContents 1. FlexibleAssemblySystems-HardwareComponentsand Features...1 1. 1 BasiccomponentsofaFAS...1 1. 1. 1Robots...1 1. 1. 2 PeripheraJequipment...4 1. 2 Classificationof'flexibleassemblysystems 5 1. 3 Examplesofindustrialinstallations...8 1. 3. 1 Mechanicalassembly...8 1. 3. 2 Printedcircuitboardassembly...9 2. IssuesinDesign,PlanningandSchedulingofFlexible Assembly...17 2. 1 FASdesignissues...18 2. 2 Networkdesignformaterialflowsystems...22 2. 3 Designforassembly...27 2. 4 Assemblyplanning 30 2. 5 Planningandscheduling...32 2. 5. 1 Machineloadingandassemblyrouting...35 2. 5. 2 Machineandvehiclescheduling 37 2. 5. 3 Planningandschedulinginelectronicsassembly...38 3. LoadingandRoutingDecisionsinFlexibleAssembly Systems 41 3. 1 Descriptionofaflexibleassemblysystem...43 3. 2 Optimizationofstationworkloadsandproductmovements. . 44 3. 3 Designandbalancingofflexibleassemblylines...50 3. 4 Numericalexamples 52 3. 5 Simultaneousloadingandrouting 56 3. 5. 1 Problemformulations...56 3. 5. 2 Aninteractiveheuristicforloadingandrouting 60 3. 5. 3 Numericalexamples...61 3. 6 Sequentialloadingandrouting...68 3. 6. 1 Problemformulations...

This book deals with stochastic combinatorial optimization problems in supply chain disruption management, with a particular focus on management of disrupted flows in customer-driven supply chains. The problems are modeled using a scenario based stochastic mixed integer programming to address riskneutral, risk-averse and mean-risk decision-making in the presence of supply chain disruption risks. The book focuses on integrated disruption mitigation and recovery decision-making and innovative, computationally efficient multi-portfolio approach to supply chain disruption management, e.g., selection of primary and recovery supply portfolios, demand portfolios, capacity portfolios, etc. Numerous computational examples throughout the book, modeled in part on realworld supply chain disruption management problems, illustrate the material presented and provide managerial insights. Many propositions formulated in the book lead to a deep understanding of the properties of developed stochastic mixed integer programs and optimal solutions. In the computational examples, the proposed mathematical programming models are solved using an advanced algebraic modeling language such as AMPL and CPLEX, GUROBI and XPRESS solvers. The knowledge and tools provided in the book allow the reader to model and solve supply chain disruption management problems using commercially available software for mixed integer programming. Using the end-of chapter problems and exercises, the monograph can also be used as a textbook for an advanced course in supply chain risk management. After an introductory chapter, the book is then divided into six main parts. Part I addresses selection of a supply portfolio; Part II considers integrated selection of supply portfolio and scheduling; Part III looks at integrated, equitably efficient selection of supply portfolio and scheduling; Part IV examines integrated selection of primary and recovery supply and demand portfolios and production and inventory scheduling, Part V deals with selection of resilient supply portfolio in multitier supply chain networks; and Part VI addresses selection of cybersecurity safequards portfolio for disruption management of information flows in supply chains.

This book deals with stochastic combinatorial optimization problems in supply chain disruption management, with a particular focus on management of disrupted flows in customer-driven supply chains. The problems are modeled using a scenario based stochastic mixed integer programming to address risk-neutral, risk-averse and mean-risk decision-making in the presence of supply chain disruption risks. The book focuses on innovative, computationally efficient portfolio approaches to supply chain disruption management, e.g., selection of primary and recovery supply portfolios, demand portfolios, capacity portfolios, etc. Numerous computational examples throughout the book, modeled in part on real-world supply chain disruption management problems, illustrate the material presented and provide managerial insights. In the computational examples, the proposed mathematical programming models are solved using an advanced algebraic modeling language such as AMPL and CPLEX, GUROBI and XPRESS solvers. The knowledge and tools provided in the book allow the reader to model and solve supply chain disruption management problems using commercially available software for mixed integer programming. Using the end-of chapter problems and exercises, the monograph can also be used as a textbook for an advanced course in supply chain risk management. After an introductory chapter, the book is then divided into five main parts. Part I addresses selection of a supply portfolio; Part II considers integrated selection of supply portfolio and scheduling; Part III looks at integrated, equitably efficient selection of supply portfolio and scheduling; Part IV examines integrated selection of primary and recovery supply (and demand) portfolios and scheduling; and Part V addresses disruption management of information flows in supply chains.