On the optimal computing budget allocation problem for large scale simulation optimization

Selecting a set that contains the best simulated systems is an important area of research. When the number of alternative systems is large, then it becomes impossible to simulate all alternatives, so one needs to relax the problem in order to find a good enough simulated system rather than simulatin...

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Veröffentlicht in:Simulation modelling practice and theory Jg. 71; S. 149 - 159
Hauptverfasser: Al-Salem, Mohammed, Almomani, Mohammad, Alrefaei, Mahmoud, Diabat, Ali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.02.2017
Schlagworte:
Large scale problems
Optimal computing budget allocation
Ordinal optimization
Simulation optimization
Ordinal optimization
Large scale problems
Simulation optimization
Optimal computing budget allocation
ISSN:1569-190X, 1878-1462
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Zusammenfassung:Selecting a set that contains the best simulated systems is an important area of research. When the number of alternative systems is large, then it becomes impossible to simulate all alternatives, so one needs to relax the problem in order to find a good enough simulated system rather than simulating each alternative. One way for solving this problem is to use two-stage sequential procedure. In the first stage the ordinal optimization is used to select a subset that overlaps with the actual best systems with high probability. Then in the second stage an optimization procedure can be applied on the smaller set to select the best alternatives in it. In this paper, we consider the optimal computing budget allocation (OCBA) in the second stage that distribute available computational budget on the alternative systems in order to get a correct selection with high probability. We also discuss the effect of the simulation parameters on the performance of the procedure by implementing the procedure on three different examples. The numerical results indeed indicate that the choice of these parameters affect its performance.
ISSN:1569-190X
1878-1462
DOI:10.1016/j.simpat.2016.05.004