Table Of ContentSpringer Optimization and Its Applications 152
Mahdi Fathi
Marzieh Khakifirooz
Panos M. Pardalos Editors
Optimization
in Large Scale
Problems
Industry 4.0 and Society 5.0
Applications
Springer Optimization and Its Applications
Volume 152
ManagingEditor
PanosM.Pardalos ,(UniversityofFlorida)
Editor-CombinatorialOptimization
Ding-ZhuDu(UniversityofTexasatDallas)
AdvisoryBoard:
J.Birge(UniversityofChicago)
S.Butenko(TexasA&MUniversity)
F.Giannessi(UniversityofPisa)
S.Rebennack(KarlsruheInstituteofTechnology)
T.Terlaky(LehighUniversity)
Y.Ye(StanfordUniversity)
AimsandScope
Optimizationhasbeenexpandinginalldirectionsatanastonishingrateduringthe
lastfewdecades.Newalgorithmicandtheoreticaltechniqueshavebeendeveloped,
thediffusionintootherdisciplineshasproceededatarapidpace,andourknowledge
ofallaspectsofthefieldhasgrownevenmoreprofound.Atthesametime,oneof
themoststrikingtrendsinoptimizationistheconstantlyincreasingemphasisonthe
interdisciplinarynatureofthefield.Optimizationhasbeenabasictoolinallareas
ofappliedmathematics,engineering,medicine,economicsandothersciences.
The series Springer Optimization and Its Applications aims to publish state-
of-the-art expository works (monographs, contributed volumes, textbooks) that
focusonalgorithmsforsolvingoptimizationproblemsandalsostudyapplications
involvingsuchproblems.Someofthetopicscoveredincludenonlinearoptimization
(convex and nonconvex), network flow problems, stochastic optimization, optimal
control, discrete optimization, multi-objective programming, description of soft-
warepackages,approximationtechniquesandheuristicapproaches.
VolumesfromthisseriesareindexedbyWebofScience,zbMATH,Mathematical
Reviews,andSCOPUS.
Moreinformationaboutthisseriesathttp://www.springer.com/series/7393
Mahdi Fathi • Marzieh Khakifirooz
Panos M. Pardalos
Editors
Optimization in Large Scale
Problems
Industry 4.0 and Society 5.0 Applications
123
Editors
MahdiFathi MarziehKhakifirooz
DepartmentofIndustrialandSystems SchoolofScienceandEngineering
Engineering TecnológicodeMonterrey
MississippiStateUniversity Monterrey,Mexico
Starkville,MS,USA
PanosM.Pardalos
DepartmentofIndustrialandSystems
Engineering
UniversityofFlorida
Gainesville,FL,USA
ISSN1931-6828 ISSN1931-6836 (electronic)
SpringerOptimizationandItsApplications
ISBN978-3-030-28564-7 ISBN978-3-030-28565-4 (eBook)
https://doi.org/10.1007/978-3-030-28565-4
MathematicsSubjectClassification:90-xx,9008,90Bxx,90Cxx,90C05,90C06,90C11,90C30,90C39,
90C90,65-xx,65K05,49-XX,49M25,40M27,49M29,49M37
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Preface
TheconceptofIndustry4.0,smartmanufacturing,orFourthIndustrialRevolution
signifies development and involves the digitalization of production processes in
industry.Itsprogressischaracterizedbycontinuousgrowthandrapidchanges,with
the goal of prospering and improving living standards through high value-added
servicesandproducts.Ontheotherhand,focusonpeopleratherthanindustrybrings
otherphenomenaintoconsiderationthatconvincegovernmentstoputsocietyatthe
center rather than industry, taking the technology as a catalyst and driver, looking
forthegeneralwelfareofcitizens,andplacingthepeopleatthecenterofIndustry
4.0.ThisviewpointofcivilizationiscalledSociety5.0.
Due to the fact that changing the working style will change our lifestyle or
viceversa,therelationshipbetweenIndustry4.0andSociety5.0isundeniable. In
Industry4.0,thegenerationofknowledgeandintelligenceisdonebyhumanswith
thehelpoftechnology;inSociety5.0,thegenerationofknowledgeandintelligence
will come from machines through artificial intelligence at the service of people.
Regardless ofthegeneratorofknowledge, information,anddata(eithersocietyor
industry), the scale of problems is big in this domain such as ageing populations,
climatechange,ridesharing,energygridmanagement,andvehicleroutingproblem.
Todealwiththesechallenges,ahugeamountofdataisneededtobecollectedand
analyzedtofullyleverageitsbenefits.Theusesofanalyticalmethods(mathematical
optimization, heuristic methods, decomposition methods, stochastic optimization,
etc.)haveafirmtrackrecordofanalyzingthevastamountsofdatatooptimizethe
planningandreal-timecontrolofprocesses.Therefore,operationsresearchmethods
inthecontentofoptimizationforlarge-scaleproblemsarewell-positionedtobenefit
Society 5.0 and Industry 4.0. However, how the solutions might transition from
theorytorealapplicationsisaquestionthatthisbookisgoingtoanswer.
Thisbookhastheaimofprovidingresourcefulthinkingandinsightfulmanage-
mentsolutiontomanychallengesthatdecision-makersmayfaceinthepreparation,
implementation, and prediction of the features of societies and industries toward
digitalization and smartness. Therefore, the book is divided into two sections: the
first covering the general perspective and challenges in smart society and industry
andthesecondcoveringsomecasestudiesandsolutionsfromoperationsresearch
v
vi Preface
perspective for large-scale challenges specific to various industry- and society-
related phenomena. Chapters from general perspective are authored by expert
people in each field, are brief, and are valuable summary key points for students,
researchers,andacademicianswhoarewillingtoputfaceonthesedomainsandget
ideatogoforfurtherinvestigation.
Thefirstgoalofthisbookistoexplainwhatthelarge-scaleproblemsinsocio-
industrialdilemmasareandwhattheoriescansupportthesechallenges.Duetothis
goal,thefirstvolumeofthisseriesofworkswaspublishedwithmorefocusonthe
theoryoflarge-scaleoptimizationinthebookentitledLargeScaleOptimizationin
SupplyChainsandSmartManufacturing:TheoryandApplications.Lateronfrom
thefeedbackofouraudience,wefoundthatbothpractitionersandacademiciansare
looking more for a learning platform which brings up open questions with related
casestudies.Hereuponthisneed,weeditedthecurrentbookasthesecondvolume
ofthisserieswiththeaimofattentionatapplicationandwithabriefintroductionof
miscellaneouschallengesinthedomainofIndustry4.0andSociety5.0.
The book is started by answering why large-scale optimization is needed for
Industry4.0andSociety5.0.Followingthat,briefchaptersdiscussashortsummary
oftheprincipalimplicationsofchallengesinvolvedinrunningsmartcityorindustry
includingbehavioralfinanceinriskmanagementoflarge-scaleproblemsinIndustry
4.0 and Society 5.0; trusting algorithms in Society 5.0; warehousing and material
handlinginIndustry4.0;product-servicesystemformanufacturingbusinessmodel
transformation by data analytics; challenges in reliability engineering problems in
Industry 4.0; the applications, mathematics, and algorithms of two-player game
theoretical approaches in optimization problems for Industry 4.0 and Society 5.0
applications;applicationsofqueueingtheoryinIndustry4.0andSociety5.0;online
controlled experiments at large scale in Society 5.0; security modeling in Society
5.0;andpedestriansimulationandtrafficmodelinginSociety5.0.
Thebriefreviewofcasestudychaptersisasfollows:
Chapter13presentsanindustrialmodelingandprogramminglanguage(IMPL)for
optimizationmodelingandestimationofindustrialprojectssuchasoilandgas,
chemicals, mining and minerals, pulp and paper, and food and beverage. It is
a highly comprehensive structure- and semantic-based language for industrial
off-andonlineoptimizationmodeling.Mostmixedintegerlinearprogramming
(MILP) and nonlinear programming (NLP) solvers are connected to IMPL to
solve a variety of optimization problems such as design, planning, scheduling,
operations,andprocesscoordinatingoptimizationproblems.
Many algorithms are categorized as “machine learning” such as support vector
machine, logistic regression, graphical models, and deep learning in Industry
4.0 and Society 5.0 which not only summarize our data but are perceived as
a learning model or classifier from the data and consequently find the hidden
pattern in data that will be seen in the future. Chapter 14 studies how to train
effectively large-scale machine learning models based on stochastic gradient
method.
Preface vii
In military logistics, there are thousands of valuable inventories to be managed.
Chapter 15 discusses about a wholesale inventory optimization model for US
Navy.
Semiconductor manufacturing is a capital-intensive industry, in which matching
the demand and capacity is a challenging decision due to the long lead time
for capacity expansion and shortening product life cycles of various demands.
Chapter 16 studies the multi-objective product-mix planning and revenue man-
agementforthemanufacturingsystemswithunrelatedparallelmachineswitha
multi-objectivegeneticalgorithm.
Decomposition techniques such as Benders decomposition have a critical role in
large-scale optimization with successful results for various large-scale mixed
integerlinear,nonlinear,convex,andnon-convexoptimizationproblems.Chap-
ter 17 proposes a framework to solve set cover problems (SCPs) with block-
angularstructuresthroughsolvingtheirsubproblemsandthendevelopamethod
forsolvinggeneralSCPs.
ManagingelectronicsupplychainsisachallengingissueinIndustry4.0,andrapid
response to customer orders is necessary to determine an effective long-term
risk mitigation strategy for these businesses. Chapter 18 proposes a risk-based
stochasticoptimizationframeworkforelectronicsupplychainsbasedonhybrid
fabrication-fulfillmentmanufacturing.
WehavemanylocationandinventorydecisionproblemsinSociety5.0.Thereisa
challengeonoptimaldecision-makingonthesparesallocationandthebudgeting
problemsinmultiple-locationinventorysystems.Chapter19analyzestheeffect
ofcustomerpatienceonmultiple-locationinventorysystems.
Health4.0dealswithhowvirtualizationandbigdataarerevolutionizinghealthcare
in modern society. In addition, more robust models in the practical healthcare
environmentaredemanded.Medicalimagingcanfacilitatediagnosis,treatment,
and surgical planning and increase clinical productivity. Artificial intelligence
(AI)techniqueshelptheaccuracyandefficiencyofimageprocessinginhealth-
care services. Chapter 20 studies two deep learning-based AI methods for
high-dimensionalmedicalimageanalysis,e.g.,tissueclassificationandmedical
imagedataaugmentation.
We are facing an exponential growth in using electrical vehicles in Society 5.0
because of their key role in sustainable development. Future smart cities affect
advanced transportation networks by global electrification. Chapter 21 studies
the comprehensive behavior of electric vehicle drivers by considering physical
characteristics of electric vehicles to evaluate the required battery power for
overcomingmechanicalresistances.
Complexsystemssuchassocialnetworksareanalyzedthroughnetworkmodeling.
Networking is a big challenge in Society 5.0. It is merged with influence
maximization (IM) problem of identifying a small subset of influential people
to maximize their spread of influence in a network. Chapter 22 discuss about
influence maximization in social networks and has many applications such as
viralmarketing,electioncampaign,counterterrorismefforts,rumorcontrol,and
salespromotions.
viii Preface
Column generation has been used successfully to solve a variety of large-scale
optimizationproblems.Chapter23discussesabouthowtobuildeffectivecolumn
generation models to solve real-world large-scale optimization problems with
applicationinairlineindustry.
Curbspacemanagementandtrafficflowinsmartcitiesaretwoessentialelements
ofthetransportationsystemthatassociatewitheachotherandaffecttheoverall
systemperformance.Thegrowthofnewmobilityoperatorsandgoodsdelivery
in urban areas results in a growing demand for pickup/drop-off access to the
curbsides.Chapter24investigatestheallocationofcurbspaceforvarioususesto
enhance the urban modality systems’ performance that can improve the overall
transportationsystemperformance.
TherearemanysparseoptimizationsinIndustry4.0suchastheelectricitydemand
predictionwhichiscrucialforbalancingthepowersupplyanddemandinsmart
powergridsinintelligentsocieties.Chapter25presentsarealcasestudyonL1
optimization for sparse structure machine learning based on electricity demand
prediction.
Mobile manufacturing is an example of rapid technology development in Society
5.0. Chapter 26 presents a dynamic programming model for analyzing the
valueofproductioncapacitymobilityandoptimizingthelogisticsoflarge-scale
productioninventorysystemswithmobileproductionunits.
Millionsofsmall,family-managednanostoresaretheprimaryoriginofconsumer-
packaged goods in many developing countries. Chapter 27 deals with a multi-
objective traveling salesman problem for deriving an effective supply strategy
forreal-lifenanostoresinmajorcities.
Wehopethatthisbookwillproveusefultoresearchers,students,andengineersin
differentdomainswhoencounterintheirworklarge-scaleoptimizationproblems
and encourage them to undertake research in this exciting and practically
importantfield.
Wewanttothankalltheauthorsinvolvedinthisprojectfortheircontributions.We
alsowanttothankthereviewers,whohavehelpedusinreviewingandimproving
severalchaptersofthisbook.
MahdiFathi Starkville,MS,USA
MarziehKhakifirooz Monterrey,Mexico
PanosM.Pardalos Gainesville,FL,USA
Contents
PartI RoadmapofOptimizationforLargeScaleProblem
LargeScaleOptimizationIsNeededforIndustry4.0andSociety5.0..... 3
LorenzGranrath
BehavioralRiskManagementofLarge-ScaleProjects ...................... 7
E.TedPrince
TrustingAlgorithmsinSociety5.0............................................. 13
J.N.Hooker
OnlineControlledExperimentsatLargeScaleinSociety5.0 .............. 17
RonKohavi
ApplicationsofQueueinginSociety5.0 ....................................... 23
MyronHlynka
DataAnalyticsEnabledSmartManufacturingBusinessModel
Transformation–AProduct-ServiceSystemsPerspective.................. 29
ThorstenWuest
ABriefOverviewofInterdictionandRobustOptimization ................ 33
LeonardoLozanoandJ.ColeSmith
Matrix-AnalyticMethods–AnAlgorithmicApproachtoStochastic
ModellingandAnalysis.......................................................... 41
Qi-MingHe
TheNextGenerationofOptimization:AUnifiedFrameworkfor
DynamicResourceAllocationProblems....................................... 47
WarrenB.Powell
OptimalPlanningofElectricPowerSystems................................. 53
I. F.AbdinandE.Zio
ix
x Contents
IdealFlowNetworkinSociety5.0.............................................. 67
KardiTeknomo
ModelingChallengesofSecuringGatesforaProtected
AreainSociety5.0 ............................................................... 71
YaelDeutsch
PartII CaseStudies
IndustrialModelingandProgrammingLanguage(IMPL)forOff-
andOn-LineOptimizationandEstimationApplications.................... 75
JeffreyD.KellyandBrennoC.Menezes
HowEffectivelyTrainLarge-ScaleMachineLearningModels? ........... 97
AvenSamarehandMahshidSalemiParizi
NavalWholesaleInventoryOptimization ..................................... 111
JavierSalmeronandEmilyM.Craparo
Smart Production by Integrating Product-Mix Planning and
RevenueManagementforSemiconductorManufacturing.................. 129
MarziehKhakifirooz,Jei-ZhengWu,andMahdiFathi
ANestedDecompositionApproachforaLargeScaleSetCovering
Problem:AModelwithaVarietyofApplicationsinIndustry4.0 ......... 165
MaryamRadmanandKouroshEshghi
RiskBasedOptimizationofElectronicsManufacturingSupplyChains .. 179
NasimNezamoddini,FaisalAqlan,andAmirhoseinGholami
TheEffectofCustomerPatienceonMultiple-Location
InventorySystems................................................................ 201
MichaelDreyfussandYahelGiat
SmartHealthinMedicalImageAnalysis ..................................... 221
HaifengWang,QianqianZhang,DaehanWon,andSangWonYoon
A Multi-layer Physic-based Model for Electric Vehicle Energy
DemandEstimationinInterdependentTransportationNetworks
andPowerSystems............................................................... 243
M.HadiAmini
InfluenceMaximizationinSocialNetworks................................... 255
ShashankShesharSingh,AjayKumar,ShivanshMishra,KuldeepSingh,
andBhaskarBiswas
SolvingLargeScaleOptimizationProblemsintheTransportation
IndustryandBeyondThroughColumnGeneration......................... 269
YanqiXu
