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Algorithms 2018,11, 68
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0.5 1 1.5 2 2.5 3 3.5
Mutation probability
0.05 0.1 0.2
Figure17.Meansand95%LSDconfidence intervalsofmutationprobability—sixmachinesperstage.
6.4. ParetoFrontCalibrationAnalysis
Figures18–20showthesolutionspaceobtainedbycalibrationexperiments. Thehorizontalaxis
represents theenergyEop consumedbythemachines. Theverticalaxis represents the timecompletion
Cmaxof jobs. Todeterminethebest individualsofallexperiments, theParetofront iscalculatedforeach
case. Eachpointrepresentsacombinationofparameters from162experimentsexplainedinSection6.1.
Eachfrontconsistsofnumberedpoints fromlowest tohighestaccordingto theirDI.Thecloser to1,
thebetter theplace in theenumeration is (seeSection3.3).
96.8
97
97.2
97.4
97.6
6,960 7,010 7,060 7,110
Eop (kW)
Figure18.Pareto front for twomachinesperstage.
44.28
44.48
44.68
44.88
45.08
12,350 12,450 12,550 12,650
Eop (kW)
Figure19.Pareto front for fourmachinesperstage.
88
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Algorithms for Scheduling Problems
- Title
- Algorithms for Scheduling Problems
- Authors
- Frank Werner
- Larysa Burtseva
- Yuri Sotskov
- Editor
- MDPI
- Location
- Basel
- Date
- 2018
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-03897-120-7
- Size
- 17.0 x 24.4 cm
- Pages
- 212
- Keywords
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Categories
- Informatik
- Technik