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Ordinal Optimization with Computing Budget Allocation for Selecting an Optimal Subset

Author

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  • Mohammad H. Almomani

    (Faculty of Science, Jerash University, Jerash 26150, Jordan)

  • Mahmoud H. Alrefaei

    (Department of Mathematics and Statistics, Jordan University of Science and Technology, Irbid 22110, Jordan)

Abstract

In this paper, we consider the problem of selecting the top m systems when the number of alternative systems is very large. We propose a sequential procedure that consists of two stages to solve this problem. The procedure is a combination of the ordinal optimization (OO) technique and optimal computing budget allocation (OCBA) method. In the first stage, the OO is used to select a subset that overlaps with the set of actual best k% systems with high probability. Then in the second stage the optimal computing budget is used to select the top m systems from the selected subset. The proposed procedure is tested on two numerical examples. The numerical tests show that the proposed procedure is able to select a subset of best systems with high probability and short simulation time.

Suggested Citation

  • Mohammad H. Almomani & Mahmoud H. Alrefaei, 2016. "Ordinal Optimization with Computing Budget Allocation for Selecting an Optimal Subset," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(02), pages 1-17, April.
    • Handle: RePEc:wsi:apjorx:v:33:y:2016:i:02:n:s0217595916500093
    DOI: 10.1142/S0217595916500093
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    References listed on IDEAS

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    1. David W. Sullivan & James R. Wilson, 1989. "Restricted Subset Selection Procedures for Simulation," Operations Research, INFORMS, vol. 37(1), pages 52-71, February.
    2. Chun-Hung Chen & Donghai He & Michael Fu & Loo Hay Lee, 2008. "Efficient Simulation Budget Allocation for Selecting an Optimal Subset," INFORMS Journal on Computing, INFORMS, vol. 20(4), pages 579-595, November.
    3. David W. Sullivan & James R. Wilson, 1989. "Corrections to “Restricted Subset Selection Procedures for Simulation”," Operations Research, INFORMS, vol. 37(4), pages 673-673, August.
    4. Chun-Hung Chen & Stephen E. Chick & Loo Hay Lee & Nugroho A. Pujowidianto, 2015. "Ranking and Selection: Efficient Simulation Budget Allocation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 45-80, Springer.
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