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Algorithms 2018,11, 54 Figure4.Market focusedstrategy. 2. ScenarioofBoundedRationalDecisionMaker Table2depicts theorderingquantitiesandprofitsunderboundedrationality for test case1 Table2.Orderingquantitiesandprofitsunderboundedrationality for test case1. CasesforBoundedRationalityConditionswithVaryingβParameter β1 β2 OrderingQuantity inMarket1 OrderingQuantity inMarket2 TotalProfits %Deviationfrom AchievableProfits 0 0 1409 1156 108,640 0.12 10 10 1409 1187 105,802 1.34 100 100 1491 1482 104,054 1.88 1000 1000 1499 1499 103,753 1.97 10,000 10,000 1499 1499 103,752 2.03 100,000 100,000 1500 1500 103,750 2.45 1,000,000 1,000,000 1500 1500 103,750 2.56 4.2. II.TestCase2 Problemsizen=4. 1. ScenarioofUniformDemandDistribution: Table3 illustrates theProductionallocationvalues. X: Table3.Productionallocationvalues for test case2. Market1 Market2 Market3 Market4 Plant1 1518 0 482 0 Plant2 0 1187.5 0 0 Plant3 0 0 968 0 Plant4 0 0 0 1608 Objective functionvalue=286,553.59. Strategicallocationofcommoncomponent is showninFigure5. 138
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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
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Algorithms for Scheduling Problems