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Algorithms 2018,11, 68
6.3.VarianceAnalysis
Varianceanalysis isapplied toevaluate thestatisticaldifferencebetweentheexperimental results
and to observe the effect of the parameters on the quality of the results. It is used to determine
factors thathaveasignificanteffectandtodiscover themost important factors. Theparametersof the
problemareconsideredas factorsandtheirvaluesas levels.Weassumethat there isno interaction
betweenfactors.
TheF-Ratio is theratiobetweenFactorMeanSquareandMeanSquareresidue.AhighF-Ratio
means that this factorsignificantlyaffects theresponse. Thep-valueshowsthestatistical significance
of the factors: p-values that are less than0.05havea statistically significant effect on the response
variable (RIBS)witha95%confidence level.Accordingto theF-Ratioandp-value, themost important
factors in thecaseof twomachinesper stageare themutationoperatorandthecrossoveroperator
variable.DFshowsthenumberofdegreesof freedom(Table10).
Table10.Relative incrementof thebestsolution(RIBS)analysisofvariancefor twomachinesperstage.
Source SumofSquares DF MeanSquare F-Ratio p-Value
A:Crossover 1747 1 1747 9.755 0.00213
B:Mutation 11,967 1 11,967 66.833 9.58×10−14
C:Crossoverprobability 1519 1 1519 8.481 0.00411
D:Mutationprobability 654 1 654 3.652 0.05782
E:Population 1732 1 1732 9.673 0.00222
Residuals 27,934 156 179
In the case of fourmachinesper stage, themost important factors are themutationoperator
andcrossoverprobability (Table11). Forsixmachinesperstage, themost important factorsare the
mutationoperatorandmutationprobability (Table12).
Table11.RIBSanalysisofvariance for fourmachinesperstage.
Source SumofSquares DF MeanSquare F-Ratio p-Value
A:Crossover 760 1 760 4.602 0.03349
B:Mutation 13,471 1 13,471 81.565 6.09×10−16
C:Crossoverprobability 1540 1 1540 31.223 1.00×10−7
D:Mutationprobability 5157 1 5157 9.326 0.00266
E:Population 3395 1 3395 20.557 1.15×10−5
Residuals 25,765 156 165
Table12.RIBSanalysisofvariance forsixmachinesperstage.
Source SumofSquares DF MeanSquare F-Ratio p-Value
A:Crossover 2587 1 2587 18.39 3.14×10−5
B:Mutation 17,785 1 17,785 126.47 2×10−16
C:Crossoverprobability 2886 1 2886 20.52 1.16×10−5
D:Mutationprobability 4840 1 4840 34.42 2.58×10−8
E:Population 2881 1 2881 20.49 1.18×10−5
Residuals 21,937 156 165
Figures 14–17 showmeans and 95%Least SignificantDifference (LSD) confidence intervals.
Figure14showstheresultsobtainedformutationoperators for two, four,andsixmachinesperstage.
WecanseethatoperatorDisplacement is thebestmutationamongthethreetestedoperators. Figure15
shows the results for the crossoveroperators,whereweobserve that thePMXoperator is thebest.
Theresults for fourandsixstagesaresimilar.
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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