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algorithms
Article
SingleMachineSchedulingProblemwithInterval
ProcessingTimesandTotalCompletion
TimeObjective
YuriN.Sotskov*andNataljaG.Egorova
UnitedInstituteof InformaticsProblems,NationalAcademyofSciencesofBelarus,SurganovaStreet6,
Minsk220012,Belarus;NataMog@yandex.by
*Correspondence: sotskov48@mail.ru;Tel.: +375-17-284-2120
Received: 2March2018;Accepted: 23April2018;Published: 7May2018
Abstract:Weconsiderasinglemachineschedulingproblemwithuncertaindurationsof thegiven
jobs. The objective function isminimizing the sumof the job completion times. We apply the
stabilityapproachto theconsidereduncertainschedulingproblemusingarelativeperimeterof the
optimalityboxasastabilitymeasureof theoptimal jobpermutation.Weinvestigatedpropertiesof
theoptimalityboxanddevelopedalgorithmsforconstructing jobpermutations thathave the largest
relativeperimetersof theoptimalitybox.Computational results forconstructingsuchpermutations
showedthat theyprovidedtheaverageerror less than0.74%for thesolveduncertainproblems.
Keywords: scheduling;uncertaindurations; singlemachine; total completiontime
1. Introduction
Sincereal-life schedulingproblemsinvolvedifferent formsofuncertainties, severalapproaches
havebeendevelopedin the literature fordealingwithuncertainschedulingproblems. Inastochastic
approach, job processing times are assumed to be randomvariableswith the knownprobability
distributions [1,2]. Ifonehasnosufficient informationtocharacterize theprobabilitydistributionof
all randomprocessingtimes,otherapproachesareneeded[3–5]. In theapproachofseekingarobust
schedule [3,6], thedecision-makerprefersaschedule thathedgesagainst theworst-casescenario.A
fuzzyapproach[7–9]allowsascheduler todeterminebest scheduleswithrespect to fuzzyprocessing
times. A stability approach [10–12] is based on the stability analysis of the optimal schedules to
possiblevariationsof thenumericalparameters. In thispaper,weapply thestabilityapproach toa
singlemachineschedulingproblemwithuncertainprocessing timesof thegiven jobs. InSection2,
wepresent thesettingof theproblemandtherelatedresults. InSection3,weinvestigateproperties
ofanoptimalityboxof thepermutationusedforprocessingthegiven jobs. Efficientalgorithmsare
derived forfindinga jobpermutationwith the largest relativeperimeter of theoptimalitybox. In
Section5,wedevelopanalgorithmforfindinganapproximatesolutionfor theuncertainscheduling
problem. InSection6,wereportonthecomputational results forfindingtheapproximatesolutions for
the tested instances. Section7 includes theconcludingremarks.
2. ProblemSettingandtheRelatedResults
Therearegivenn jobsJ = {J1, J2,..., Jn} tobeprocessedonasinglemachine. Theprocessing
time piof the job Ji∈J cantakeanyrealvalue fromthegivensegment [pLi ,pUi ],where pUi ≥ pLi >0.
Theexactvalue pi∈ [pLi ,pUi ]of the jobprocessing timeremainsunknownuntil completing the job
Ji ∈J . LetRn+denote a set of all non-negativen-dimensional real vectors. The set of all possible
vectors (p1,p2, . . . ,pn)= p∈Rn+of the jobprocessingtimes ispresentedas theCartesianproductof
Algorithms 2018,11, 66;doi:10.3390/a11050066 www.mdpi.com/journal/algorithms21
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Buch Algorithms for Scheduling Problems"
Algorithms for Scheduling Problems
- Titel
- Algorithms for Scheduling Problems
- Autoren
- Frank Werner
- Larysa Burtseva
- Yuri Sotskov
- Herausgeber
- MDPI
- Ort
- Basel
- Datum
- 2018
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-03897-120-7
- Abmessungen
- 17.0 x 24.4 cm
- Seiten
- 212
- Schlagwörter
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Kategorien
- Informatik
- Technik