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Algorithms 2018,11, 43
• Scenario4: hightardiness factor (T=0.4)andlargeduedaterange(R=1.2).
Usingtheseparameters, 6000 instancesweregenerated inGroup1,dividedin60classes. That is,
five levelsofnumberof jobs, three levelsofnumberofmachines, four scenariosand100 instances
perclass (5×3×4×100=6000). ForGroup2,9600 instancesweregenerated,dividedin96classes,
six levelsofnumberof jobs, four levelsofnumberofmachines, fourscenariosand100 instancesper
class (6×4×4×100=9600). Atotalof15,600 instancesweregeneratedandtheirparametersare
summarized inTable3.
Table3.Parameters forgenerationof the instances.
Group1 Group2
Numberof jobs 5,6,7,8,10 15,20,30,50,80,100
Numberofmachines 2,3,5 5,10,15,20
Scenarioconfigurations Scenario1:T=0.2andR=0.6;
Scenario2:T=0.2andR=1.2;
Scenario3:T=0.4andR=0.6;
Scenario4:T=0.4andR=1.2
Numberof instancesperclass 100 100
Numberof instancespergroup 6000 9600
Totalnumberof instancessolved 15,600
5.2. Experiment1: RelativeComparisonof theProposedHeuristics
In thisfirstpartof theresultsevaluation,arelativecomparisonof theproposedheuristicswas
conducted. Themostcommonmeasureused in the literature tocompare theperformancesof solution
methods is therelativepercentagedeviation(RPD) (e.g., [22,25]),which,adaptedto themaximization
problemaddressed, is calculatedas:
RPD=( nbestJIT−nhJIT
nbestJIT )100, (14)
wherenbestJIT is thebestsolutionfoundandn h
JIT theheuristicsolutionevaluated. Thelowerisamethod’s
RPD, thebetter is itsperformance,withaRPDofzero indicatingthat themethodprovidedthebest
solutionfound(orachievedatie).
Tocompare theheuristicsperformances forGroup1’s instances, thesolutionofanenumeration
method (EM), basedon [25],wasusedas a reference (see thepseudo-code inAlgorithm12). It is
important tonote that theEMprovidesareferencesolutionto theproblemand,althoughit listedn!
possiblepermutations (sequences)of jobs, itdoesnotwarrantanoptimalsolutionsince theschedule
iscomposedbysequencingandtiming. That is, for thesamesequence, it ispossible tofindseveral
scheduleswithdifferentstarting times foreachoperation, therebyresulting inmanypossibilities for
thesamenumberof just-in-time jobs,ascanbeseen inFigure1.
66
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book Algorithms for Scheduling Problems"
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