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Algorithms 2018,11, 76
Table5.Roundedperformancedegradationandranking.
Criteria Strategy
Rand BC PESS OPTI PHEFT OHEFT
Montage Cmax 3.189 0.010 0.009 0.039 0.005 0.011
cpw 0.209 0.173 0.100 0.196 0.050 0.093
cps 0.001 0.305 0.320 0.348 0.302 0.306
Mean 1.133 0.163 0.143 0.194 0.119 0.137
Rank 6 4 3 5 1 2
Ligo Cmax 0.044 0.043 0.012 0.012 0.001 0.002
cpw 0.580 0.542 0.059 0.059 0.002 0.013
cps 0.040 0.040 0.012 0.013 0.002 0.002
Mean 0.221 0.208 0.028 0.028 0.002 0.005
Rank 6 5 3 4 1 2
All test cases Cmax 1.616 0.027 0.011 0.025 0.003 0.006
cpw 0.394 0.357 0.079 0.128 0.026 0.053
cps 0.020 0.173 0.166 0.180 0.152 0.154
Mean 0.677 0.186 0.085 0.111 0.060 0.071
Rank 6 5 3 4 1 2
6.2. PerformanceProfile
In theprevious section,wepresented theaverageperformancedegradationsof the strategies
over threemetricsandtest cases.Now,weanalyzeresults inmoredetail. Oursamplingdatawere
averagedovera largescale.However, thecontributionofeachexperimentvarieddependingonits
variabilityoruncertainty [30–32]. Toanalyze theprobabilityofobtainingresultswithacertainquality
andtheir contributorsonaverage,wepresent theperformanceprofilesof thestrategies.Measures
of resultdeviationsprovideuseful informationforstrategiesanalysisandinterpretationof thedata
generatedbythebenchmarkingprocess.
Theperformanceprofileδ(τ)pτ isanon-decreasing,piecewiseconstant functionthatpresents the
probability thataratioγ iswithina factorτof thebest ratio [33]. The functionδ(τ) is thecumulative
distributionfunction. Strategieswith largerprobabilitiesδ(τ) forsmallerτwillbepreferred.
Figure5showstheperformanceprofilesof thestrategiesaccordingtototalcompletiontime, inthe
intervalτ=[1. . .1.2], toprovideobjective informationforanalysisofa test set.
(a) (b)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 1.021.04 1.06 1.08 1.1 1.12 1.141.16 1.18 1.2
߬ Rand
BC
PESS
OPTI
PHEFT
OHEFT
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 1.02 1.04 1.06 1.08 1.1 1.12 1.14 1.16 1.18 1.2
߬ Rand
BC
PESS
OPTI
PHEFT
OHEFT
Figure5.Cmaxperformanceprofile,τ=[1. . .1.2]. (a)Montage; (b)Ligo.
Figure5adisplaysresults forMontageworkflows. PHEFThadthehighestprobabilityofbeing
thebetter strategy. Theprobability that itwas thewinneronagivenproblemwithin factorsof1.02of
thebest solutionwasclose to0.9. Ifwechose tobewithinafactorof1.1as thescopeofour interest,
188
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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