Exact and Heuristic Scheduling Algorithms

202 Pages·2020·2.021 MB·English

by  Frank Werner (editor), Larysa Burtseva (editor), Yuri Sotskov (editor)

E
x
a
c
t
a
n
d
H
e
u
r
is
t
ic
S
c
h
e
d
u
lin
g
A
lg
o
r
it
h
m
s
•
F
r
a
n
k
W
er Exact and
n
e
r, L
a
r
y
s
a
B Heuristic
u
r
t
s
e
v
a
a
n
d Scheduling
Y
u
r
i S
o
t
s
k
o
v Algorithms
Edited by
Frank Werner, Larysa Burtseva and Yuri Sotskov
Printed Edition of the Special Issue Published in Algorithms
www.mdpi.com/journal/algorithms
Exact and Heuristic Scheduling
Algorithms

Exact and Heuristic Scheduling
Algorithms
SpecialIssueEditors
FrankWerner
LarysaBurtseva
YuriSotskov
MDPI•Basel•Beijing•Wuhan•Barcelona•Belgrade•Manchester•Tokyo•Cluj•Tianjin
SpecialIssueEditors
FrankWerner LarysaBurtseva YuriSotskov
Otto-von-Guericke-University UniversidadAutonomadeBaja NationalAcademyofSciencesof
Magdeburg CaliforniaMexicali Belarus
Germany Mexico Belarus
EditorialOffice
MDPI
St.Alban-Anlage66
4052Basel,Switzerland
This is a reprint of articles from the Special Issue published online in the open access journal
Algorithms (ISSN 1999-4893) (available at: https://www.mdpi.com/journal/algorithms/special
issues/SchedulingAlgorithms).
Forcitationpurposes,citeeacharticleindependentlyasindicatedonthearticlepageonlineandas
indicatedbelow:
LastName,A.A.; LastName,B.B.; LastName,C.C.ArticleTitle. JournalNameYear,ArticleNumber,
PageRange.
ISBN978-3-03928-468-9(Pbk)
ISBN978-3-03928-469-6(PDF)
(cid:2)c 2020bytheauthors. ArticlesinthisbookareOpenAccessanddistributedundertheCreative
Commons Attribution (CC BY) license, which allows users to download, copy and build upon
publishedarticles,aslongastheauthorandpublisherareproperlycredited,whichensuresmaximum
disseminationandawiderimpactofourpublications.
ThebookasawholeisdistributedbyMDPIunderthetermsandconditionsoftheCreativeCommons
licenseCCBY-NC-ND.
Contents
AbouttheSpecialIssueEditors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
Prefaceto”ExactandHeuristicSchedulingAlgorithms”. . . . . . . . . . . . . . . . . . . . . . . ix
MarcoAntonioJua´rezPe´rez,RodolfoEleazarPe´rezLoaiza,
PerfectoMalaquiasQuinteroFlores,OscarAtrianoPonceandCarolinaFloresPeralta
AHeuristicAlgorithmfortheRoutingandSchedulingProblemwithTimeWindows: ACase
StudyoftheAutomotiveIndustryinMexico
Reprintedfrom:Algorithms2019,12,111,doi:10.3390/a12050111 . . . . . . . . . . . . . . . . . . . 1
JuliaLangeandFrankWerner
On Neighborhood Structures and Repair Techniques for Blocking Job Shop Scheduling
Problems
Reprintedfrom:Algorithms2019,12,242,doi:10.3390/a12110242 . . . . . . . . . . . . . . . . . . . 21
JiheneKaabi
Modeling and Solving Scheduling Problem with m Uniform Parallel Machines Subject to
UnavailabilityConstraints
Reprintedfrom:Algorithms2019,12,247,doi:10.3390/a12120247 . . . . . . . . . . . . . . . . . . . 49
AlessandroAgnetis,FabrizioRossiandStefanoSmriglio
SomeResultsonShopSchedulingwithS-PrecedenceConstraintsamongJobTasks
Reprintedfrom:Algorithms2019,12,250,doi:10.3390/a12120250 . . . . . . . . . . . . . . . . . . . 61
JoseM.FraminanandRainerLeisten
LinkingSchedulingCriteriatoShopFloorPerformanceinPermutationFlowshops
Reprintedfrom:Algorithms2019,12,263,doi:10.3390/a12120263 . . . . . . . . . . . . . . . . . . . 73
YuriN.Sotskov,NataljaM.MatsveichukandVadzimD.Hatsura
Two-Machine Job-Shop Scheduling Problem to Minimize the Makespan with Uncertain Job
Durations
Reprintedfrom:Algorithms2020,13,4,doi:10.3390/a13010004 . . . . . . . . . . . . . . . . . . . . 95
V´ıctorPacheco-Valencia,Jose´AlbertoHerna´ndez,Jose´Marı´aSigarretaandNodariVakhania
SimpleConstructive,Insertion,andImprovementHeuristicsBasedontheGirdingPolygonfor
theEuclideanTravelingSalesmanProblem
Reprintedfrom:Algorithms2020,13,5,doi:10.3390/a13010005 . . . . . . . . . . . . . . . . . . . . 141
IliaTarasov,AlainHaitandOlgaBattaia
AGeneralizedMILPFormulationforthePeriod-AggregatedResourceLevelingProblemwith
VariableJobDuration
Reprintedfrom:Algorithms2020,13,6,doi:10.3390/a13010006 . . . . . . . . . . . . . . . . . . . . 171
v

About the Special Issue Editors
Frank Werner studied Mathematics from 1975–1980and graduated fromthe TechnicalUniversity
Magdeburg (Germany) with honors. He defended his Ph.D. thesis on the solution of special
schedulingproblemsin1984with‘summacumlaude’andhishabilitationthesisin1989. In1992,
he received a grant from the Alexander-von-Humboldt Foundation. Currently, he works as
an extraordinary professor at the Faculty of Mathematics of the Otto-von-Guericke University
Magdeburg(Germany). Heistheauthororeditoroffivebooksandhaspublishedmorethan280
papers in international journals. He is on the Editorial Board of 17 journals, in particular, he is
the Editor-in-Chief of Algorithms and an Associate Editor of the International Journal of Production
ResearchandJournalofScheduling. HewasamemberoftheProgramCommitteeofmorethan75
internationalconferences.Hisresearchinterestsareoperationsresearch,combinatorialoptimization,
andscheduling.
LarysaBurtsevagraduatedinEconomicCybernetics(1975)attheRostovStateUniversity(Russia)
anddefendedherPh.D.thesisonControlinTechnicalSystems(TechnicalCybernetics),1989,atthe
RadioelectronicsUniversityofKharkov(Ukraine). Since2000,sheworksasaprofessorresearcher
attheEngineeringInstituteofUniversidadAuto´nomadeBajaCalifornia,Mexicali,Mexico. Shehas
led the Laboratory of Scientific Computation since 2005. She has more than 70 publications in
international journals and proceedings of congresses and 6 book chapters. Her research interests
are discrete optimization, particularly combinatorial optimization, scheduling, and also packing
problemsfordifferentapplications. SheisamemberoftheNationalSystemofResearchersofthe
NationalCouncilforScienceandTechnologyofMexico(SNICONACYT)andaregularmemberof
TheMexicanAcademyofComputation(AMEXCOMP).
YuriN.Sotskov,Prof. D.Sc.,finishedsecondaryschoolwithagoldmedalin1966andgraduated
fromtheFacultyofAppliedMathematicsoftheBelarusianStateUniversityinMinskin1971.In1980,
hedefendedhisPh.D.thesisattheInstituteofMathematicsoftheNationalAcademyofSciencesof
BelarusinMinsk.In1991,hedefendedhisD.Sc.thesisattheInstituteofCyberneticsoftheNational
AcademyofSciencesofUkraineinKyiv.HehasthetitleofProfessorinApplicationofMathematical
ModelsandMethodsinScientificResearch(RussianAcademyofSciences,1994).Hecurrentlyworks
asaprincipalresearcherattheUnitedInstituteofInformaticsProblemsoftheNationalAcademy
of Sciences of Belarus in Minsk. He has more than 350 publications: five scientific monographs,
twotextbooks, morethan200papersininternationaljournals, books, andconferenceproceedings
inEnglishonappliedmathematics,operationsresearch,graphtheory,andscheduling. Heisonthe
Editorial Board of five journals. He has supervised eight Ph.D. scholars (defended). In 1998, he
receivedtheNationalPrizeofBelarusinScienceandEngineering.
vii

Preface to ”Exact and Heuristic Scheduling
Algorithms”
Optimal scheduling is an important area of operations research and in particular of decision
making. This research field covers a large variety of different solution approaches designed for
particularproblems. Itisclearthatefficientalgorithmsarehighlydesirableforlarge-sizereal-world
schedulingproblems. DuetotheNP-hardnessofthemajorityofschedulingproblems,practitioners
oftenprefertouserathersimpleschedulingalgorithmssincelarge-sizeproblemsusuallycannotbe
solvedexactlyinanacceptabletime. However,fastheuristicsmayproduceschedulesthefunction
valuesofwhichmightbefarawayfromtheoptimalones.
ThisbookisbasedonaSpecialIssueentitled“ExactandHeuristicSchedulingAlgorithms”. It
isafollow-upbookofthebooklet,“AlgorithmsforSchedulingProblems”publishedin2018(ISBN
978-3-03897-119.1)andcontainsboththeoreticalandpracticalaspectsintheareaofdesigningefficient
exact and heuristic scheduling algorithms. We hope that the articles contained in this book will
stimulateresearcherstofindnewpracticaldirectionsforimplementingtheirschedulingalgorithms.
InresponsetotheCallforPapers, weselectedeightsubmissions, allofwhichareofhighquality,
reflectingtheinterestintheareaofdevelopingexactandheuristicalgorithmstosolvereal-world
productionplanningandschedulingproblems. Asarule, allsubmissionshavebeenreviewedby
threeexpertsintheDiscreteOptimizationandSchedulingarea.
Thefirstarticledealswithareal-worlddistributionproblemarisinginthevehicleproduction
industryinMexico.Thisproblemincludesboththeloadingandoptimalroutingofeachauto-carrier,
where both the capacity constraints and the time windows have to be taken into account. The
authorssuggestatwo-stageheuristicalgorithm.First,arouteisconstructedbyanoptimalinsertion
heuristic. Thenafeasibleloadingisdetermined. Forthecomputationalexperiments,twoscenarios
weregeneratedwith11differentinstancesofthedemand.Hereoneinstancedescribesarealproblem
ofalogisticscompanyinMexico. Thealgorithmallowedtoobtaintheroutesandtheloadingofthe
vehiclesforproblems with4400vehiclesand 44dealershipsconsidered. Theresultsshowed that
thedevelopedalgorithmwassuccessfulinminimizingtotaltravelingdistance,loading/unloading
operationsandtransportationcosts.
The second article is devoted to the heuristic solution of job shop scheduling problems with
blocking constraints and minimization of total tardiness. This problem has many applications
in manufacturing, but also in railway scheduling. Due to the NP-hardness of the problem, the
development of heuristics is of high relevance. In particular, a permutation-based heuristic is
derived. Theauthorsusethreeinterchange-andshift-basedtransitionschemes. However,typical
neighborhoodsappliedtojobshopproblemsoftengenerateblockinginfeasiblesolutions.Therefore,
the core of this paper is to present two repair mechanisms that always generate neighbors being
feasiblewithrespecttotheblockingconstraints.Characteristicsforthecomplexneighborhoodssuch
astheaveragedistanceofaneighborareanalyzed. Thesuggestedneighborhoodsareembedded
into a simulated annealing algorithm. Detailed computational results have been given for the
modifiedLawranceinstanceswithupto30jobsand15machinesaswellasforhardtraininspired
instanceswithupto20jobsand11machines.AcomputationalcomparisonwasmadewiththeMIP
formulationgiveninanearlierpaperbyLangeandWernerinJournalofScheduling(2018). Itturned
outthatforsmallinstances,theheuristicoftenobtainedanoptimalornear-optimalsolution,while
forlargerinstancesseveralofthebestknownsolutionsbytheMIPsolverhavebeenimproved.
ix