Operational Scheduling in a Multi-Actor Environment using Multiagent Systems

196 Pages·2015·4.99 MB·English

FacultyofTechnology,PolicyandManagement
INPARTIALFULFILLMENTOFTHEREQUIREMENTSFORTHE
DEGREEOF
MasterofScience
Operational Scheduling in a Multi-Actor Environment
using Multiagent Systems
AMTPLBT
MScDissertationof:
MatthijsBrouns
Chair:
Prof.Dr.Ir.C.Chorus
FirstSupervisor:
Dr.H.Aldewereld
SecondSupervisor:
Dr.S.vanCranenburgh
ThirdSupervisor:
Dr.M.deWeerdt
ExternalSupervisor:
M.JansenMSc
September2015

i
Preface
This thesis is submitted in partial fulfilment of the requirements for the degree
of Master of Science in SystemsEngineering,PolicyAnalysisandManagement. It
containsworkdonefromFebruarytoSeptember2015.
This project was conceived during my time working for Systems Navigator.
SystemsNavigatorwasworkingonadecisionsupportsystemtoaidthescheduling
staff of liquid bulk terminals in creating schedules. During a meeting with my
supervisor,HuibAldewereld,heproposedtoassesswhetherMultiagentsystems
couldbesuccessfullyemployedtocreateschedulingoptionsandproposetheseto
plannersworkingatliquidbulkterminals.
Writingthisthesisanddoingtherelatedresearchhasbeenahard,butsatisfying
processinwhichmanyofthebasicaspectsofMultiagentsystems-cooperationand
negotiation-werepractised.
Iwouldliketothankmysupervisor,Dr. HuibAldewereld,fortheinspiration
andguidancehehasofferedintheprocess. ThanksalsotoProf. CasparChorus
and Dr. Sander van Cranenburgh for their immense expertise on the subject of
discretechoice modellingand therandomregretminimizationmodel especially.
Furthermore,lotsofthanksareduetoDr. MathijsdeWeerdt,whoofferedgreat
insights in Multiagent systems, Mechanism Design and Algorithmics, and was
willingtoremainasupervisorinmygraduationcommitteeeventhoughofficially
notbeingapartofmyfaculty.
Furthermore,IwouldliketothankMartJansen,withoutwhomIwouldnot
havebeenabletofinishthisthesis. Hisknowledgeregardingliquidbulkterminals
andschedulingproblemsingeneralhasprovedessentialintheemergenceofthis
research. Finally, I also want to thank Systems Navigator and its employees for
providingmewiththemeansfordoingthisresearch.
MB
Delft
September2015
ii
Summary
Liquid bulk is one of the largest industries of the modern world, and efficient
schedulingisneededforliquidbulkterminalstoremaincompetitive. Acommon
solutionfortheplanningefficiencyproblemistoapplyplanningalgorithms,rather
thanhumanplannersforcreatingschedules. However,replacinghumanplannersby
algorithmsisoftenadifficultproblemanditsimplementationisoftenobstructed
bothbymanagementandtheplanningdepartments. Inordertostillachieveefficient
schedules,attentionhasshiftedfromplanningalgorithmsintendedtoreplacethe
humanplanner,toschedulingsupportsystemswhichaimtoassisttheplannerin
makingefficientschedules. Acoreaspectofbothautomatedschedulingsystemsas
wellasschedulingsupportsystemsisanoptimizationengine,toallowthehuman
plannertoquicklygeneratealternativescheduleoptions.
Inrecentyears,literaturehasemergedintroducingtheconceptofMultiagent
systemsforplanningandscheduling. Multiagentschedulingseemstobeasuitable
approachforschedulinginliquidbulkterminalssincetheagentsprovideanatural
metaphor for the scheduling problem in a liquid bulk terminal. This is mainly
becauseaterminalservesvesselswhichareownedbyasetofcustomerswhichare
largelyuninterestedinthevesselsofothercustomers. Multiagentsystemscan,in
suchascenario,offerflexibilitywithregardstooptimalitycriteriawhichtraditional
schedulingsystemscannot.
The main goal of this study was to determine whether Multiagent Systems
(MAS) could be effectively applied for proposing schedule allocation options to
assistschedulersinliquidbulkterminals.
Theresearchstartedwithanextensivebackgroundstudy,inwhichitwasfound
thatthereareanumberofstakeholdersinvolvedintheschedulingprocess,whichhave
partiallyconflictinggoals. Themainrelevantstakeholdersaretheterminalandits
customers,bothconsistingofseveralindependentdepartmentsorentities. Themain
goalsoftheterminalareachievingahighterminalutilization,havingalowimpact
on surroundings, having low pressure on operations, and not paying demurrage
costs. Themaingoalsfromthecustomersideareachievingshortturnaroundtimes,
andmaintainingproductquality. Bycombiningthestakeholderrequirementswith
information regarding the processes, infrastructure, and products at liquid bulk
terminals, itwasfoundthattheschedulingproblemresemblesaspecificversion
ofthejob-shopproblem: JMPT|pmtn,prec,r ,s |multi. Sincethisproblemis
i ij
stronglyNP-harditisgenerallyinfeasibletofindanoptimalsolutionasthesizeof
theprobleminstancegrows,andefficientalgorithmsareneededtofindsolutions.
Afterformallydefiningtheschedulingproblemaspresentinliquidbulkter-
minals, this thesis moved on to determine the applicability of several common
Multiagentschedulingsolutionsbasedonfourdimensions: whetherthesolution
providesagoodanalogytotheschedulingproblem,whetherthesolutioncoulddeal
withthearrivalanddepartureofvesselsovertime,whetherthesolutioncouldincor-
poratesharedinfrastructurebetweenroutesandwhetherterminalpreferencescould
bemodelled. ItwasfoundthatCombinatorialAuctionsprovidedthebestfitonthe
iii
schedulingproblem. Duetoexistingcontractstructuresbetweenterminalsandtheir
customersitprovedimpossibletoincorporateanadditionalpaymentstructurein
theschedulingproblem,makingitinfeasibletodesigntheauctiontobetruthfuland
strategy-proof. Nonetheless,thisisnotconsideredaproblemasstrategicbehaviour
caneasilybeverified.
Forincorporatingmulti-attributeoptimizationsseveraldesigndirectionswere
exploredincludingPareto-optima,hardconstraintsanddiscretechoicemodels. It
was found that only discrete choice models offered the flexibility needed in this
case. Two types of discrete choice models were proposed and implemented: a
UtilityMaximizationmodel,aswellasaRegretMinimizationmodel. Empirical
studies have shown that in certain cases, a regret minimizing model provides a
significantlybetterfitonthepreferencedata,meaningthattheyarebetterableto
capturethetrade-offsmadebyhumanswhencomparingalternatives. Asthegoalof
thisschedulingsystemistoassisttheplannerinmakingallocationchoices,itcan
benefitgreatlyfromthesefeatures. Amajordrawbackoftheregretmodelisthat
findingtheregretforallalternativesisanO(n2)operation,ratherthananO(n)
operation. Forsolutionmethodswherethisposedabottleneck,ahybridregret/
utilitymodelwasproposedandimplemented.
The auction structure was implemented using a minor modification on the
ContractNetProtocol. ThefinalMultiagentSystemsconsistsofthreeagenttypes:
aRouteFinderagent,aVesselagent,andaPlanneragent. TheRouteFinderagent
isresponsibleforfindingroutesforspecificvesselsandproductsontheterminal,
aswellasformakingaccurateestimationsontheprocessingtimesonthatroute.
ThisisimplementedusingacombinationofYen’salgorithmandamodificationof
Dijkstra’sshortestpathalgorithm. TheVesselagentusesteroutesfromtheRoute
Finderagenttodetermineavaluationontheroutesandsubmitsthesevaluations
asbidstothePlanneragent. ThePlanneragentretrievesallbidsandusestheseto
createfinalscheduleproposals. ThisisdonebysolvingtheWinnerDetermination
Problem(WDP)oftheauction.
SeveralalgorithmswereimplementedandtestedforsolvingtheWDP,includ-
ingaBranchandBoundalgorithm,informedsearchmethodsandtwoSimulated
Annealingapproaches. ItwasfoundthatSimulatedAnnealing,implementedasa
searchamongalternativecompletesolutions,wasthemostfeasiblesolutionstrategy.
Typically,itprovidedsolutionswhich,onaverage,achievea50%higherutilitymodel
value than a First-Come First-Served Heuristic in three minutes. Furthermore,
theSimulatedAnnealingprocesscanbestoppedatanymomentandstillprovidea
betteralternativesolution.
Allalgorithmswereimplementedusingautility-maximisingaswellasaregret-
minimisingstrategy. Itwasfoundthatthetoptenresultsfromthesedifferentmodels
typicallydifferedfromeachotherwithaneditdistanceoffive. Furtherempirical
studieswouldbeneededtodeterminewhetherregret-basedmodelsprovidebetter
acceptedsolutionsinpracticethantheirutility-basedcounterparts.
Contents
Preface i
Summary ii
1 Introduction 1
2 ResearchMethodology 7
I Background 11
3 LiquidBulkTerminals 13
3.1 Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 OperationsatLiquidBulkTerminals . . . . . . . . . . . . . . . . 19
3.3 LiquidBulkProducts . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4 TerminalInfrastructure . . . . . . . . . . . . . . . . . . . . . . . 22
3.5 SystemEnvironment . . . . . . . . . . . . . . . . . . . . . . . . 22
3.6 FormalSchedulingDefinition . . . . . . . . . . . . . . . . . . . 23
3.7 SchedulingComplexity . . . . . . . . . . . . . . . . . . . . . . . 26
3.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4 MultiAgentSystemsforplanningandscheduling 29
4.1 Multi-AgentSystemArchitectures . . . . . . . . . . . . . . . . . 30
4.2 MASinliquidbulkscheduling . . . . . . . . . . . . . . . . . . . 33
4.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5 CombinatorialAuctions 43
5.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2 Truthfulness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.3 Biddingrulesinliquidbulkscheduling . . . . . . . . . . . . . . . 46
5.4 Clearingrulesinliquidbulkscheduling . . . . . . . . . . . . . . 49
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
II AlgorithmDesignandImplementation 55
6 AlgorithmArchitecture 57
6.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
iv
CONTENTS v
6.2 RolesModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
6.3 Agentmodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
6.4 Servicesmodel . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6.5 Acquaintancesmodel . . . . . . . . . . . . . . . . . . . . . . . . 63
6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
7 RouteFinderAgent 65
7.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
7.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
7.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
8 VesselAgent 75
9 PlannerAgent 79
9.1 WinnerDeterminationProblem . . . . . . . . . . . . . . . . . . 80
9.2 AllocationEvaluation . . . . . . . . . . . . . . . . . . . . . . . . 93
9.3 Feasibilitydetection . . . . . . . . . . . . . . . . . . . . . . . . . 102
9.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
IIIValidationandEvaluation 107
10 SystemTesting 109
10.1 Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
10.2 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
10.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
11 Experimentation 119
11.1 ExperimentalDesign . . . . . . . . . . . . . . . . . . . . . . . . 119
11.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
11.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
12 Conclusions 133
Bibliography 139
AppendixA:OperationsatLiquidBulkTerminals 151
AppendixB:Operationstiming 155
AppendixC:TerminalInfrastructure 157
AppendixD:SystemEnvironment 161
AppendixE:ExampleTerminalDefinition 167
vi CONTENTS
AppendixF:GaiaModels 173
AppendixG:SetTheorySymbols 179
ListofFigures 180
ListofTables 182
ListofAcronyms 185