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SpringerSeriesinAdvancedManufacturingSeriesEditorProfessorD.
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)LyesBenyoucef·BernardGrabotEditorsArtificialIntelligenceTechniquesforNetworkedManufacturingEnterprisesManagement123LyesBenyoucef,PhDINRIANancy-GrandEstCOSTEAM/LGIPMISGMPBat.
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Coverdesign:eStudioCalamar,Figueres/BerlinPrintedonacid-freepaperSpringerispartofSpringerScience+BusinessMedia(www.
springer.
com)SeriesEditor'sForewordTheglobalisationofmanufacturinghasledtotheneedfornewmanagementandcontrolapproachesforcoordinatingspatiallyandtemporallydistributedopera-tions.
WewelcomethislatestadditiontoourAdvancedManufacturingSeries.
Thenewbook,whichaddressestheapplicationofAItechniquestothemanagementofnetworkedenterprises,iseditedbytwoexpertsintheareaofproductionandop-erationsmanagement,ProfessorLyesBenyoucefandProfessorBernardGrabot.
WeareparticularlypleasedasLyesandBernardwereourcollaboratorsintherecentlycompletedEU-fundedNetworkofExcellenceprojectonInnovativePro-ductionMachinesandSystems(I*PROMS).
WethankthemforcompletingthebookbothasacontributiontothefieldofintelligentmanufacturingsystemsandasanimportantdeliverableofourNetwork.
DucPhamEditor,SpringerSeriesinAdvancedManufacturingCardiff,UnitedKingdomJanuary2010PrefaceTheglobaleconomyandtherecentdevelopmentsininformationandcommunica-tiontechnologieshavesignificantlymodifiedthebusinessorganizationofenter-prisesandthewaythattheydobusiness.
Newformsoforganizationssuchasex-tendedenterprisesandnetworkedenterprises(alsocalledsupplychainnetworks)appearandtheyarequicklyadoptedbymostleadingenterprises.
Itiswellknownthat"competitioninthefuturewillnotbebetweenindividualorganizationsbutbetweencompetingsupplychains"(Simchi-Levietal.
,2003).
Thus,businessop-portunitiesarecapturedbygroupsofenterprisesinthesamenetwork.
Themainreasonforthischangeistheglobalcompetitionthatforcesenterprisestofocusontheircorecompetences(i.
e.
,todowhatyoudothebestandletothersdotherest).
AccordingtoavisionaryreportofManufacturingChallenges2020conductedintheUSA(NationalResearchCouncil,1998),thistrendwillcontinueandoneofthesixgrandchallengesofthisreportistheabilitytoreconfigurenetworkeden-terprisesrapidlyinresponsetochangingneedsandopportunities.
Althoughtheresultingnetworkedenterprisesaremorecompetitive,thetasksforplanning,man-agingandoptimizingaremuchmoredifficultandcomplex.
Whilealliance-likeenterprisenetworkswiththeunderlyingsupplynetworkrepresenttremendousbusinessopportunities,theyalsomaketheinvolvedenter-prisesfacegreateruncertaintiesandrisks.
Firstly,networksorsupplychainshavetobemodifiedordissolvedoncethebusinessopportunitiesevolveordisappear.
Secondly,changesormajorperturbationsatoneenterprisemaypropagatethroughthewholenetworktootherenterprisesandhenceinfluencetheirperformance.
Theevolutionfromsingleenterprisewithahighverticalrangeofmanufacturetowardsenterprisenetworksoffersnewbusinessopportunitiesespeciallyforsmallandmediumenterprisesthatareusuallymoreflexiblethanlargercompanies.
How-ever,inordertobesuccessful,performanceandexpectedbenefitshavetobecare-fullyevaluatedandbalancedinordertobecomeapartneroftherightnetworkofenterprisesfortherighttask.
Alltheseissueshavetobetakenintoaccountinor-dertofindanefficient,flexibleandsustainablesolution.
ApromisingapproachPrefaceviiiforthatpurposeistocombineanalyticalmethodsandknowledge-basedap-proaches,inadistributedcontext.
Artificialintelligence(AI)techniqueshavebeenusedinmultiplesegmentsofthenetworkedenterprises.
Theyhavetakenaprominentroletointegratepeople,informationandproductsacrossdynamicnetworkedenterpriseboundariesinclud-ingmanagementofvariousmanufacturing,logisticsandretailingoperationssuchaspurchasing,manufacturing,warehousinganddistributionofgoods.
Decisionsinvolvingcustomerprofiling,newproductdevelopment,retailmarketing,andsalespatternsareimmenselyrefinedusingbusinessintelligencetools.
Further,assuchdecisionshaveanimpactontheoverallnetworkenterpriseprocesses,itisimportantthatbusinessintelligencetoolsshouldalsobelinkedtonetworkeden-terprisemanagementapplications.
Thisbookaimstoalignlatestpractice,innovationandcasestudieswithaca-demicframeworksandtheories.
Itwillincludethelatestresearchresultsandef-fortsatdifferentlevelsincludingquick-responsesystem,theoreticalperformanceanalysis,andperformanceandcapabilitydemonstration,hopingtocovertheroleofemergingAItechnologiesinmodeling,evaluatingandoptimizingnetworkedmanufacturingenterprisesactivitiesatdifferentlevels.
Sixteenchapterswereselectedafterapeerreviewprocess.
Theywererevisedinaccordancewiththesuggestionsandrecommendationsfromthereviewers.
TheyaddressprominentconceptsandapplicationsofAItechnologiesinmanagingnetworkedmanufacturingenterprises.
Chapter1,byE.
Oztemel,providesinformationonintelligentmanufacturingsystems.
Italsoincludesananalysisonhistoricalprogressofmanufacturingsys-temsaswellasabriefreviewoftraditionalmanufacturingsystems.
FundamentaltechnologiesofAIarereviewedinordertoestablishthebaselineforintelligentmanufacturingsystems.
Moreover,basiccharacteristicsofintelligentmanufactur-ingandrespectivearchitecturesareprovided.
Someexamplesoftheapplicationsofintelligentmanufacturingsystemsarealsohighlightedinthischapter.
Chapter2,byA.
J.
Soroka,describesanetworkedsystemdevelopedforauto-matingthegathering,processingandmanagementofproductfaultknowledge.
Suchasystemcanremovetheneedformanualprocessingoffaultreportsandtheassociatedproblems.
Areactiveagentarchitecturebasedupontheconceptoffinitestateautomata(FSA)isimplementedusingtheJavaprogramminglanguage.
ItalsoshowsthattheFSA-basedagentarchitectureissuitableforapplicationinthisparticularproblemdomain,duetothereactivenatureofanFSA.
Chapter3,byH.
Dingetal.
,presentsamulti-agent-basedsimulationtoolwithdescriptionsoftheoverallarchitecture,modelingelements,operationalpolicies,etc.
Thetoolhasbeenusedinacommercialprojectwithaleadinghigh-techmanufacturer.
Thecomplexrelationshipsbetweenservicelevels,inventorycost,transportationcost,andforecastingaccuracyarewellstudied.
Theprojectresultsshowthatnetworkedenterprisescanreallygetbetterinsightfromsuchaquantita-tiveanalysisandwouldbeabletoidentifysolidopportunitiesforcostsavingandperformanceimprovement.
PrefaceixChapter4,byOuzroutetal.
,focusesonunexpectedswingsindemandandonunexpectedexceptions(problemofproduction,problemoftransportation,etc.
),whichareimportantcoordinationandcommunicationissuesinsupplychainman-agement.
Thechapteranalysessomeoftheexistingapproachesandworkandde-scribesanagent-baseddistributedarchitectureforthedecision-makingprocess.
Theagentsinthisarchitectureuseasetofnegotiationprotocols(suchasfirmheu-ristic,recursiveheuristic,collaborativeplanningandforecastingreplenishmentnegotiationprotocol)tocollectivelymakedecisionsinashorttime.
Thearchitec-tureisvalidatedonanindustrialcasestudy.
Chapter5,byKesharietal.
,presentsaconceptualframeworkofaweb-services-basede-collaborativesystemtoestablishareal-timeinformation-sharingplatformforenablingcollaborationamongsimilartypesofmanufacturingfirms.
Themainideaofthechapteristointegratetheprocessplanningandschedulingactivitieswiththeproposedsystemconsideringoutsourcingasaviabletechniqueofenhancingmachineutilizationandsystem'sperformance.
Anillustrativeexam-pleisconsideredthatdemonstratetheworkingmechanismoftheproposedframe-work.
Chapter6,byOunnarandPujo,proposesanewapproachforcustomer-supplierrelationshipcontrol,inwhichthepartnershipisconsideredinthecontextofanas-sociationofpotentialsupplierswithinanetwork:anisoarchiccontrolmodelforasupplychainnetworkbasedonaholonicarchitecture.
Thedecision-makingmechanismisproducedusinganautonomouscontrolentity.
Animplementationofthesimulationofsuchasystemisdoneviaadistributedsimulationenvironmenthighlevelarchitecture.
Acasestudyispresented.
Chapter7,byA.
Dolguietal.
,discussestheproblemsoflot-sizingandse-quencingunderuncertaintiesforasinglemachineoraflow-shop.
Thecoreofthechapterisacasestudyformulti-productlot-sizingandsequencingofflow-shoplinesunderuncertainties.
Twomaintypesofuncertaintiesareconsidered:break-downs(randomleadtime)andrejects(randomyield).
Theobjectiveistomaxi-mizetheprobabilitythatcustomerdemandswillbesatisfiedatthegivenduedate.
Amathematicalmodeloftheproblemandsomeheuristicandmeta-heuristicap-proachesarediscussed.
Chapter8,byM.
Souieretal.
,presentsacomparativestudyofagroupofmeta-heuristics,includingtaboosearch,antcolonyoptimization,geneticalgorithms,particleswarmoptimization,electromagneticmeta-heuristic,andsimulatedan-nealing,againstthemodifieddissimilaritymaximizationmethodforajob-shoproutingproblempickedfromliterature.
Fivecriteriaareselectedforperformanceevaluationandcomparison,namely:productionrate,machineutilizationrate,ma-terialhandlingutilizationrate,workinprocess,andcycletime.
Thenumericalre-sultsdemonstratethatPSOandGAgeneratethebestresultspracticallyinallcases.
Chapter9,byD.
Sánchezetal.
,proposesthehybridizationofevolutionaryal-gorithms,wellknownfortheirmulti-objectivecapabilities,withasupplychainsimulationmoduleinordertodeterminetheinventorypolicy(order-pointoror-Prefacexder-level)ofasingleproductsupplychain,takingintoaccounttwoconflictingob-jectives:maximizingcustomerservicelevelandminimizingtotalinventorycost.
Fouralgorithms(SPEA-II,SPEA-IIb,MOPSOandNSGA-II)areevaluatedonfivedifferentsupplychainconfigurationstodeterminewhichalgorithmgivesthebestresultsandmakesthebestuseofthesimulator.
TheresultsindicatethatSPEA-2favorsarapidconvergenceandthatmodifyingitscrossoveroritsarchivetruncationrule(variantSPEA-IIb)mayimprovetheresultsevenfurther.
Chapter10,byD.
D'AddonaandR.
Teti,considersthedevelopmentandim-plementationofamulti-agenttoolmanagementsystem(MATMAS)forautomatictoolprocurement.
Thedesign,functioning,andperformanceofdiverseflexibletoolmanagementstrategies(FTMS)integratedintheMATMSareillustrated.
TheMATMSoperatesintheframeofanegotiation-basedmultiple-suppliernetworkwhereaturbinebladeproducer(customer)requiresdressingofworn-outcubicbo-ronnitridegrindingwheelsfromtheexternaltoolmanufacturer.
ThediverseFTMSparadigms,configuredasdomain-specificproblem-solvingfunctionsoper-atingwithintheMATMSintelligentagentandholdingtheresponsibilityforopti-mumtoolinventorysizingandcontrol,aretestedbytoolinventorymanagementsimulationsandcomparedwithrealindustrialcases.
Chapter11,byN.
RezgandS.
Dellagi,presentstwostudiesinvestigatingnewintelligentintegratedmaintenanceandproductionorservicestrategies,whichdealwithcomplexreliabilityproblems.
Thefirststudydescribesasequentialcon-strainedlinear-quadraticstochasticproduction-planningprobleminwhicharan-domdemandmustbesatisfiedandthesinglemachineissubjecttorandomfailure.
Aminimalrepairisperformedateveryfailure,withpreventivemaintenanceac-tionsscheduledaccordingtothemanufacturingsystemhistorysoastoreducethefailurefrequency.
Thesecondstudyproposesafailurelawthatcanvaryovertime,notonlywithrespecttothepreventivemaintenanceactions,butalsoasafunctionofchangesinoperatingand/orenvironmentalconditions.
Thegoalistodetermineintelligentlythenumberofpreventivemaintenanceactions,whichmustbeper-formedinordertominimizethecostunderathresholdavailabilityconstraint.
Chapter12,byT.
Yooetal.
,combinesglobalrandomsearchusingnestedpar-titions(NP)withstatisticalselectionusingoptimalcomputingbudgetallocation(OCBA)todesignaninnovativealgorithmcalledNP+OCBA.
Asanon-trivialil-lustration,thedevelopedNP+OCBAalgorithmisappliedtothemulti-passsched-ulingproblem.
Anewmulti-passschedulingframeworkispresentedthatmini-mizesthenumberofrulestobeevaluatedbyusinganNPmethod,andminimizesthetotalnumberofsimulationreplicationsbyusingOCBAmethod.
Theeffi-ciencyandeffectivenessoftheproposedNP+OCBAaredemonstratedbycompar-ingitsperformancewiththatofthreemethods,respectivelyequalallocation,standaloneOCBAandCOMPASS.
Chapter13,byB.
Desmetetal.
,discussessomeintelligentsolutionapproachesusedtooptimizesafetystocksinnetworkedmanufacturingsystems.
Theseap-proachesarebasedonnormalapproximationmodelsfortheinvolvedcriticalsafetystockparameters.
TheproposedapproximationmodelsarefirsttestedonPrefacexismallexamplesystemslikedistributionsystemsandassemblysystems,andtheningenericnetworkedmanufacturingsystems.
Moreover,theyarebenchmarkedwithresultsobtainedfromdiscrete-eventsimulation.
Asthevarioussimulationsshow,theproposedapproximationsprovetoberatherconservativeandprovidegoodupperboundsontherequiredsystemsafetystocks.
Chapter14,byS.
Karnouskosetal.
,considerstheproblemofhowtodeploywebservicesonshop-floordevicestoconnectthemtoenterprisesystems.
Morespecifically,itconsidersthecaseofacentrallymanagedpopulationofdevicesthatarelocatedatdifferentsites,dynamicdiscoveryofdevicesandtheservicestheyoffer,nearreal-timecross-siteinteraction,theirinteractionwithbusinessproc-essesanddistributedsystemmanagement.
Theresultsshowthatthedynamicna-tureoftheshop-floorcanbeutilizedefficientlytoplanfurtherproductionordersandevenimplementlast-minutechangesontheproductionlineusingreal-timedata(real-timereconfigurationbasedontheapplicationneeds).
Chapter15,byA.
W.
Colomboetal.
,illustratesanoverviewoftheengineeringapproaches,methodsandtoolsthathavebeenspecifiedanddevelopedwithintheEuropeanResearchandDevelopmentprojectSOCRADES(www.
socrades.
eu).
Theresultsofthefirstsetofsuccessfulapplicationsintheareaofelechtrome-chanicalassemblysystems,extendingtheconceptstogeographicallydistributedservice-orientedproductionsitesaredemonstrated.
Chapter16,byJ.
Barataetal.
,addressestheproblemofshop-flooragilitypre-sentingasfundamentalcornerstonefortrueagilityandresponsivenessofanenter-prisewilingtoparticipateinhighlydynamiccollaborativeorganizationsandsup-plychains.
Thefeasibilityofthearchitectureproposedisdemonstratedinapilotimplementationinanear-realshop-floor.
EmergingwebstandardssuchasDPWSareusedtoguaranteecross-layer/abstractioninteroperabilityensuringthattheshop-floorreactspositivelytoadjustmentsinthesupplychain.
Wehopeyouwillenjoytheresultoftheseefforts.
ReferencesSimchi-LeviD,KaminskyP,Simchi-LeviE(2003)Designingandmanagingthesupplychain:concepts,strategiesandcasestudies.
McGraw-HillNationalResearchCouncil(1998)Visionarymanufacturingchallengesfor2020.
Committeeonvisionarymanufacturingchallenges,boardonmanufacturingandengineeringdesign,com-missiononengineeringandtechnicalsystems.
NationalacademypressLyesBenyoucefBernardGrabotMetz,FranceTarbes,FranceJanuary2010January2010AcknowledgmentsWewishtoplaceonrecordourspecialthankstoSeriesEditorProf.
D.
T.
PhamandSeniorEditorialAssistantMissClaireProtheroughfortheirvaluableguidanceandsupportduringtheentireprocessofeditingthebook.
WeofferourthankstotheSpringereditorialteamfortheiractiveroleandsupport.
Wewouldliketothankallreviewersforprovidingin-depthcommentsandconstructivecriticisms,andtheauthorsforcontributingtheirhigh-qualitymanu-scripts.
Withoutyourhelpitwouldhavebeenimpossibletoproducethisbook.
Fi-nally,wethanktheI*Promsmembers,anetworkofexcellenceforinnovativepro-ductionmachinesandsystemsfundedbytheEuropeanCommissionunderitsFramework6Programme(www.
iproms.
org).
ManythankstoDr.
RiteshKumarSinghandMr.
AkramZouggari,INRIAre-searchers,fortheirexcellentsupportinreviewingthebook.
Wehopeyouwillenjoytheresultoftheseefforts.
LyesBenyoucefandBernardGrabotContents1IntelligentManufacturingSystemsE.
Oztemel.
11.
1Introduction.
11.
2TraditionalManufacturingSystems.
31.
3ChangesinManufacturingSystems:aHistoricalPerspective.
61.
4ArtificialIntelligenceandIntelligentManufacturingSystems.
131.
4.
1TechnologiesofArtificialIntelligence.
131.
4.
2IntelligentManufacturingSystems.
221.
5PropertiesofIntelligentManufacturingSystems.
251.
6ArchitectureofIntelligentManufacturingSystems.
271.
7HolonicManufacturingSystems.
331.
8ApplicationsofIntelligentManufacturingSystems.
371.
9Conclusions.
39References.
392Agent-basedSystemforKnowledgeAcquisitionandManagementWithinaNetworkedEnterpriseA.
J.
Soroka.
432.
1Agent-basedandRelatedSystems.
432.
1.
1OriginsofAgentResearch442.
1.
2DefinitionofanAgent.
462.
1.
3AgentArchitectures.
472.
1.
4AgentTypesandApplications502.
1.
5MachineLearningforGenerationofKnowledgeBases532.
2ProductFaultKnowledgeAcquisitionandManagement572.
2.
1AutomatingKnowledgeBasemanagement.
572.
2.
2AnalysisofKnowledgeBaseManagementProcess.
582.
3AgentSystemforKnowledgeAcquisitionandManagement.
632.
3.
1UserAgent.
652.
3.
2ServerAgent.
70Contentsxvi2.
3.
3Testing.
792.
4Conclusions81References813Multi-agentSimulation-basedDecisionSupportSystemandApplicationinNetworkedManufacturingEnterprisesH.
Ding,W.
Wang,M.
QiuandJ.
Dong873.
1Introduction873.
2LiteratureReview883.
2.
1SimulationMethods893.
2.
2Multi-agentSimulation893.
2.
3ToolsandApplications.
903.
3.
ProblemDescriptionandApproach.
913.
3.
1PlatformArchitecture.
923.
3.
2Multi-agentSupplyChainSimulationModel933.
4ModelingandAnalysis.
943.
4.
1BaselineSimulationModel953.
4.
2ScenarioAnalysis.
953.
4.
3InventoryControlPolicy.
963.
4.
4ForecastAccuracy.
973.
4.
5Build-to-planvs.
Build-to-order.
993.
4.
6ProcurementPolicy1003.
4.
7TruckUtilization1013.
5ConclusionsandPerspectives.
103References1044ACollaborativeDecision-makingApproachforSupplyChainBasedonaMulti-agentSystemY.
Ouzrout,A.
Bouras,E.
-H.
NfaouiandO.
ElBeqqali1074.
1Introduction1084.
2DistributedSimulationandSupplyChainManagement.
1104.
2.
1Decision-makingandMulti-agentApproaches.
1114.
2.
2Multi-agentSystemsSimulation1114.
3TheSupplyChainModeling.
1124.
3.
1SupplyChainModelingMethodology.
1134.
3.
2TheSafetyInventoryCaseStudy.
1144.
4TheMulti-agentArchitecture1164.
4.
1TheModelingPrinciple.
1164.
4.
2Architecture.
1174.
4.
3NegotiationProtocols.
1204.
5IndustrialCaseStudy.
1224.
6ConclusionandPerspectives125References126Contentsxvii5Web-services-basede-CollaborativeFrameworktoProvideProductionControlwithEffectiveOutsourcingA.
Keshari,M.
K.
TiwariandR.
Teti.
1295.
1Introduction.
1305.
2LiteratureReview1315.
3DesignofWeb-services-basede-CollaborativeFramework.
1335.
3.
1TheProposedFramework.
1335.
3.
2DesignofSystemComponentsofModel.
1345.
3.
3TheMathematicalModel1395.
3.
4OverallObjectiveFunction1425.
4ProtocolsofFunctionalAgents.
1435.
4.
1MachineAgentProtocol.
1435.
4.
2ProductionControllingAgentProtocol.
1445.
4.
3TaskManagementAgent(TMA)Protocol.
1455.
4.
4HierarchyofStepsinWeb-services-basede-CollaborativeSystem1455.
5ResultsandDiscussion1455.
5.
1Sample-sortSimulatedAnnealingAlgorithm1485.
5.
2ExperimentalAnalysis1485.
6Conclusions.
157References.
1586IsoarchicandMulti-criteriaControlofSupplyChainNetworkF.
OunnarandP.
Pujo.
1616.
1Introduction.
1616.
2SupplyChainManagementLimits1636.
3ControlofaDynamicLogisticNetwork:IsoarchicandMulti-criteriaControl.
1646.
3.
1DescriptionoftheInteractingEntities.
1656.
3.
2DefinitionofSelf-organizedControl.
1666.
3.
3SupportStructure:AutonomousControlEntity1716.
4DistributedSimulationofaDynamicLogisticNetwork1746.
4.
1High-levelArchitectureComponents.
1746.
4.
2IntegrationoftheDEVS-ACEModelsinHigh-levelArchitectureEnvironment.
1756.
5ExperimentsviaSimulation.
1756.
6ConclusionandFutureWork.
178References.
1797SupplyChainManagementUnderUncertainties:Lot-sizingandSchedulingRulesA.
Dolgui,F.
GrimaudandK.
Shchamialiova1817.
1Introduction.
1817.
2ShortPresentationofLot-sizingandSchedulingProblems.
1847.
2.
1BasicModelsandExtensions.
187Contentsxviii7.
2.
2Lot-sizingandSchedulingUnderUncertainties.
1917.
2.
3OptimizationTechniques1967.
3ACaseStudy1977.
3.
1DescriptionoftheCaseStudy.
1977.
3.
2RepresentationofUncertainties2007.
3.
3ObjectiveFunction.
2027.
3.
4DecompositionforOptimization.
2037.
3.
5NumericalExample.
2107.
3.
6NumericalResults2147.
4Conclusions216References2178Meta-heuristicsforReal-timeRoutingSelectioninFlexibleManufacturingSystemsM.
Souier,A.
HassamandZ.
Sari2218.
1Introduction2228.
2LiteratureReview2238.
3Job-shop.
2258.
3.
1Job-shopProblem.
2258.
3.
2SimulationofaFlexibleManufacturingSystemEnvironment.
.
.
.
.
.
.
2268.
4DissimilarityMaximizationMethodandModifiedDMMRules2288.
4.
1DissimilarityMaximizationMethodforReal-timeRoutingSelection.
2288.
4.
2ModifiedDissimilarityMaximizationMethodforReal-timeRoutingSelection.
2298.
5Meta-heuristicsforJob-shopRouting.
2308.
5.
1AntColonyOptimization.
2308.
5.
2SimulatedAnnealing.
2318.
5.
3ParticleSwarmsOptimization.
2328.
5.
4GeneticAlgorithms2348.
5.
5TabooSearch.
2358.
5.
6Electromagnetism-likeMethod.
2368.
6PerformanceEvaluationofRoutingSelectionMethods.
2378.
6.
1SystemSimulationWithoutPresenceofBreakdown.
2378.
6.
2SystemSimulationWithPresenceofBreakdown.
2418.
7Conclusions246References2479Meta-heuristicApproachesforMulti-objectiveSimulation-basedOptimizationinSupplyChainInventoryManagementD.
Sánchez,L.
AmodeoandC.
Prins.
2499.
1Introduction2499.
2LiteratureReview2519.
3ProblemFormulation.
253Contentsxix9.
4ImplementationofSelectedEvolutionaryAlgorithms.
2569.
4.
1Non-dominatedSortingGeneticAlgorithmII.
2569.
4.
2StrengthParetoEvolutionaryAlgorithmII.
2589.
4.
3StrengthParetoEvolutionaryAlgorithmIIb.
2599.
4.
4Multi-objectiveParticleSwarmOptimization.
2609.
5ComputationalExperimentsandAnalysis.
2629.
5.
1EvaluationCriteria2629.
5.
2ParametersUsed.
2639.
5.
3ExperimentalResults.
2639.
6Conclusion.
267References.
26810DiverseRisk/CostBalancingStrategiesforFlexibleToolManagementinaSupplyNetworkD.
D'AddonaandR.
Teti27110.
1Introduction.
27110.
2Multi-agentToolManagementSystem27210.
3FlexibleToolManagementStrategies.
27510.
3.
1CurrentInventory-LevelDecisionMaking(INVADAPT)27710.
3.
2Fixed-HorizonInventory-LevelDecisionMaking(I-FUTURE).
.
27910.
3.
3Variable-HorizonInventory-LevelDecisionMaking(INVADAPT_NS)28010.
4ToolInventoryManagementSimulations28110.
4.
1SimulationThroughINVADAPT.
28710.
4.
2SimulationThroughI-FUTURE29410.
4.
3SimulationThroughINVADAPT_NS.
30110.
5TestCaseApplications.
30910.
6ConclusionsandFutureWork.
312References.
31211IntelligentIntegratedMaintenancePoliciesforManufacturingSystemsN.
RezgandS.
Dellagi.
31511.
1Introduction.
31511.
2OptimalMaintenancePolicyConsideringtheInfluenceoftheProductionPlanontheDeteriorationoftheManufacturingSystem.
31811.
2.
1ProblemStatement31811.
2.
2ProblemFormulation.
31911.
2.
3InfluenceofManufacturingSystemDeteriorationontheOptimalProductionPlan32411.
2.
4OptimizationoftheMaintenancePolicy.
32611.
3IntelligentPeriodicPreventiveMaintenancePolicyinFiniteHorizonwithanAdaptiveFailureLaw.
33011.
3.
1ProblemDescription.
330Contentsxx11.
3.
2ModelFormulationandIntelligentDeterminationoftheOptimalSolution.
33211.
3.
3NumericalExample.
33511.
4Conclusions338References33912EnhancingtheEffectivenessofMulti-passSchedulingThroughOptimizationviaSimulationT.
Yoo,H.
-B.
ChoandE.
Yücesan.
34112.
1Introduction34212.
2Multi-passSchedulingUsingNestedPartitionsandOptimalComputingBudgetAllocation.
34612.
2.
1TheProposedMulti-passSchedulingFramework.
34612.
2.
2TheOuterLoop:NestedPartitions.
34612.
2.
3TheInnerLoop:OptimalComputingBudgetAllocation.
34812.
3ImplementationofNestedPartitionsandOptimalComputingBudgetAllocation34912.
3.
1NestedPartitions:PartitioningStrategy34912.
3.
2NestedPartitions:SamplingStrategy.
35112.
3.
3NestedPartitions:BacktrackingStrategy.
35112.
3.
4OptimalComputingBudgetAllocation:RankingandSelectionStrategy35212.
3.
5PerformanceofNestedPartitionsandOptimalComputingBudgetAllocation.
35312.
4ExperimentalDesignandAnalysis.
35412.
4.
1ExperimentalAssumptions.
35412.
4.
2ExperimentalDesign.
35612.
4.
3ExperimentalResultsfortheProbabilityofCorrectSelection.
.
.
.
.
.
35712.
5Conclusions363References36413IntelligentTechniquesforSafetyStockOptimizationinNetworkedManufacturingSystemsB.
Desmet,E.
-H.
AghezzafandH.
Vanmaele36713.
1Introduction36713.
2Multi-echelonInventoryControlinNetworkedManufacturingSystems.
36913.
3LiteratureReview37413.
4SystemSafetyStockOptimizationinn-echelonDistributionSystems37713.
4.
1IntroductionofaTwo-echelonDistributionSystem37813.
4.
2CharacterizationoftheBack-orderServiceTimeintheCentralWarehouse.
38013.
4.
3CharacterizationoftheActualRetailerReplenishmentLeadTime382Contentsxxi13.
4.
4SystemSafetyStockOptimizationinaTwo-echelonDistributionSystem.
38413.
5SystemSafetyStockOptimizationinn-echelonAssemblySystems.
.
.
.
38613.
5.
1IntroductionofaTwo-echelonAssemblySystem.
38613.
5.
2CharacterizationoftheBackorderServiceTimeforaSubsetofComponents.
38913.
5.
3CharacterizationoftheIncomingServiceTimetotheAssembly.
39013.
5.
4CharacterizationoftheActualAssemblyLeadTime.
39113.
5.
5SystemSafetyStockOptimizationinatwo-echelonAssemblySystem.
39213.
5.
6DistributionSystemasSpecialCaseoftheAssemblySystem.
.
.
.
.
.
39413.
6SystemSafetyStockOptimizationinGenericNetworks39513.
6.
1IntroductionofaSpanningTreeSystem39513.
6.
2CharacterizationofActualReplenishmentLeadTimesinaSpanningTreeSystem.
40213.
6.
3SystemSafetyStockOptimizationforaSpanningTreeSystem.
.
.
41513.
6.
4ExtensiontoGenericSystems.
41613.
7Conclusions.
418References.
41914Real-worldServiceInteractionwithEnterpriseSystemsinDynamicManufacturingEnvironmentsS.
Karnouskos,D.
Savio,P.
Spiess,D.
Guinard,V.
TrifaandO.
Baecker.
.
.
42314.
1Motivation.
42414.
2Real-worldAwareness.
42614.
2.
1DeviceIntegrationProtocols.
42614.
2.
2Device-to-BusinessCoupling.
42814.
2.
3IntegratingHeterogeneousDevices.
42914.
3EnterpriseIntegration43014.
4IntegratingManufacturingEquipmentwiththeSOCRADESIntegrationArchitecture43214.
5TowardsDynamicAdaptation43514.
5.
1Simulation43714.
5.
2Self-healingMechanisms43814.
5.
3Self-optimizingMechanisms.
43914.
6ConceptValidationinPrototypes44014.
6.
1MachineMonitoring,DynamicDecisionandOrderAdaptation.
.
.
.
44114.
6.
2TheFutureShopFloor:MashupofHeterogeneousService-oriented-architectureDevicesandServices.
44414.
6.
3DynamicSupplyChainManagementAdaptation44614.
6.
4TamingProtocolHeterogeneityforEnterpriseServices44914.
6.
5EnergyMonitoringandControlviaRepresentationalStateTransfer45114.
7DiscussionandFutureDirections454Contentsxxii14.
8ConclusionsandFutureWork455References45515FactoryoftheFuture:AService-orientedSystemofModular,DynamicReconfigurableandCollaborativeSystemsA.
-W.
Colombo,S.
KarnouskosandJ.
-M.
Mendes.
45915.
1Introduction.
46015.
2TheEmergenceofCooperatingObjects.
46115.
3TheCross-layerService-oriented-architecture-drivenShopFloor.
.
.
.
.
.
.
46315.
4DynamicReconfigurationofaService-oriented-architecture-basedCollaborativeShopFloor.
46515.
4.
1Methodology46615.
4.
2Example.
46815.
5AnalysisBehindtheEngineeringMethodsandTools.
46915.
5.
1ApplyingFunctionalAnalysistoValidateServiceCompositionPathsinHigh-levelPetri-net-basedOrchestrationModels46915.
5.
2Example.
47215.
6AService-oriented-architecture-basedCollaborativeProductionManagementandControlSystemEngineeringApplication.
47415.
7ConclusionsandFutureWork479References48016AService-orientedShopFloortoSupportCollaborationinManufacturingNetworksJ.
Barata,L.
RibeiroandA.
-W.
Colombo.
48316.
1Introduction48316.
2AgilityinManufacturing48516.
3CollaborativeNetworks.
48716.
4Service-oriented-architectureasaMethodtoSupportAgilityandCollaboration48816.
5Architecture49316.
5.
1TheRoleofComponentization49316.
5.
2ServiceExposure,CompositionandAggregation.
49516.
5.
3TheRoleofOrchestrationinControl,MonitoringandDiagnosis49616.
6AnImplementation.
49816.
6.
1ManufacturingDeviceServiceImplementation.
49816.
6.
2CoalitionLeaderServiceImplementation.
49816.
6.
3ApplicationExample:aCollaborativePick-and-placeOperation.
49916.
7Conclusions501References501Index.
505