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15. May,G.; Stahl, B.; Taisch,M.; Prabhu,V.Multi-objectivegenetic algorithm for energy-efficient job shop
scheduling. Int. J.Prod. Res. 2015,53, 7071–7089. [CrossRef]
16. Zhang,R.;Chiong,R.Solvingtheenergy-efficient jobshopschedulingproblem:Amulti-objectivegenetic
algorithm with enhanced local search for minimizing the total weighted tardiness and total energy
consumption. J.Clean. Prod. 2016,112, 3361–3375. [CrossRef]
17. Mouzon,G.;Yildirim,M.B.;Twomey, J.Operationalmethodsforminimizationofenergyconsumptionof
manufacturingequipment. Int. J.Prod. Res. 2007,45, 4247–4271. [CrossRef]
18. Dai,M.;Tang,D.;Giret,A.;Salido,M.A.;Li,W.D.Energy-efficient schedulingforaflexibleflowshopusing
an improvedgenetic-simulated annealing algorithm. Robot. Comput. Integr. Manuf. 2013, 29, 418–429.
[CrossRef]
19. Mansouri, S.A.; Aktas, E.; Besikci,U.Green schedulingof a two-machineflowshop: Trade-off between
makespanandenergyconsumption.Eur. J.Oper. Res. 2016,248, 772–788. [CrossRef]
20. Hecker,F.T.;Hussein,W.B.;Paquet-Durand,O.;Hussein,M.A.;Becker,T.Acasestudyonusingevolutionary
algorithmstooptimizebakeryproductionplanning.ExpertSyst.Appl. 2013,40, 6837–6847. [CrossRef]
21. Hecker,F.T.; Stanke,M.;Becker,T.;Hitzmann,B.ApplicationofamodifiedGA,ACOandarandomsearch
procedure tosolve theproductionschedulingofacasestudybakery.ExpertSyst.Appl. 2014,41, 5882–5891.
[CrossRef]
22. Fonseca, C.M.; Fleming, P.J. An overview of evolutionary algorithms in multiobjective optimization.
Evol.Comput. 1995,3, 1–16. [CrossRef]
23. Hosseinabadi,A.A.R.; Siar,H.; Shamshirband, S.; Shojafar,M.;Nasir,M.H.N.M.Using thegravitational
emulation local searchalgorithmtosolve themulti-objectiveflexibledynamic jobshopschedulingproblem
inSmallandMediumEnterprises.Ann.Oper. Res. 2015,229, 451–474. [CrossRef]
24. Deb,K.;Pratap,A.;Agarwal,S.;Meyarivan,T.Afastandelitistmultiobjectivegeneticalgorithm:NSGA-II.
IEEETrans. Evol. Comput. 2002,6, 182–197. [CrossRef]
25. Jaimes,A.L.;Coello,C.A.C. InteractiveApproachesApplied toMultiobjectiveEvolutionaryAlgorithms.
InMulticriteriaDecisionAidandArtificial Intelligence;Doumpos,M.,Grigoroudis,E.,Eds.; JohnWiley&Sons,
Ltd.: Chichester,UK,2013. [CrossRef]
26. Lu,L.;Anderson-Cook,C.;Lin,D.Optimaldesignedexperimentsusingapareto frontsearchfor focused
preferenceofmultipleobjectives.Comput. Stat.DataAnal. 2014,71, 1178–1192. [CrossRef]
27. Wagner,T.;Trautmann,H. Integrationofpreferences inhypervolume-basedmultiobjectiveevolutionary
algorithmsbymeansofdesirability functions. IEEETrans. Evol. Comput. 2010,14, 688–701. [CrossRef]
28. Baker,K.R. Introduction toSequencingandScheduling; JohnWiley&Sons,Ltd.:NewYork,NY,USA,1974.
29. Pinedo,M.Scheduling: Theory,Algorithms, andSystems;PrenticeHall:UpperSaddleRiver,NJ,USA,2002;
586p.
30. Abyaneh,S.H.;Zandieh,M.Bi-objectivehybridflowshopschedulingwithsequence-dependentsetuptimes
andlimitedbuffers. Int. J.Adv.Manuf. Technol. 2012,58, 309–325. [CrossRef]
31. Graham,R.L.;Lawler,E.L.;Lenstra, J.K.;Kan,A.H.G.R.Optimizationandapproximation indeterministic
sequencingandscheduling:Asurvey.Ann.Discret.Math. 1979,5, 287–326. [CrossRef]
32. Larrañaga,P.;Kuijpers,C.M.H.;Murga,R.H.; Inza, I.;Dizdarevic,S.Geneticalgorithmsfor the travelling
salesmanproblem:Areviewofrepresentationsandoperators.Artif. Intell. Rev. 1999,13, 129–170. [CrossRef]
33. Tan,K.C.;Lee,L.H.;Zhu,Q.L.;Ou,K.Heuristicmethods forvehicle routingproblemwith timewindows.
Artif. Intell. Eng. 2001,15, 281–295. [CrossRef]
34. Gog,A.; Chira,C.ComparativeAnalysis of RecombinationOperators inGeneticAlgorithms for the Travelling
SalesmanProblem; Springer: Berlin/Heidelberg,Germany,2011;pp.10–17.
35. Hwang,H.Animprovedmodel forvehicleroutingproblemwithtimeconstraintbasedongeneticalgorithm.
Comput. Ind. Eng. 2002,42, 361–369. [CrossRef]
36. Prins,C.Asimpleandeffectiveevolutionaryalgorithmfor thevehicle routingproblem.Comput.Oper. Res.
2004,31, 1985–2002. [CrossRef]
37. Starkweather,T.;McDaniel, S.;Mathias,K.E.;Whitley,C.Acomparisonofgenetic sequencingoperators.
InProceedingsof the4thInternationalConferenceonGeneticAlgorithms;Belew,R.K.,Booker,L.B.,Eds.;
MorganKaufmann: SanFrancisco,CA,USA,1991;pp.69–76.
38. TheRProject forStatisticalComputing.Availableonline: https://www.r-project.org/(accessedon4March
2017).
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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