Efficient Simulation Budget Allocation With Bound Information
This paper proposes a bound-based simulation budget allocation (BSBA) procedure for solving ranking and selection (R&S) problems in simulation optimization. For many practical applications, strict bounds on system performances can be obtained through empirical and theoretical approaches. These b...
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2019
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ph-ateneo-arc.gsb-pubs-10022022-03-22T08:25:23Z Efficient Simulation Budget Allocation With Bound Information Li, Haidong Xu, Xiaoyun Zhao, Yaping This paper proposes a bound-based simulation budget allocation (BSBA) procedure for solving ranking and selection (R&S) problems in simulation optimization. For many practical applications, strict bounds on system performances can be obtained through empirical and theoretical approaches. These bounds provide additional information which may help solve R&S problems. In this paper, a new method of objective function estimation is proposed using both bound information and simulation outputs. This new estimation method is demonstrated to be particularly effective. To solve R&S problems, several asymptotic optimal allocation rules are also derived. Using these allocation rules, a BSBA procedure is proposed to achieve high efficiency in identifying the best design. Numerical experiments are provided to examine the performance of the proposed BSBA procedure. The computational results show that BSBA outperforms three compared allocation procedures, especially when bounds are tight or the simulation budget is small. 2019-01-01T08:00:00Z text https://archium.ateneo.edu/gsb-pubs/3 https://ieeexplore.ieee.org/abstract/document/8675337?casa_token=1B5V8SdZvwYAAAAA:azZh4w_1RqMdeCy4F7opiSxV0r8aSZiBhs_ZWdYuugSbDTe96OasEUlRSG2tqld5SQVTEkSbZbw Graduate School of Business Faculty Publications Archīum Ateneo Risk Assesment and Reduction Creativity Innovation Culture Business Administration, Management, and Operations |
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Risk Assesment and Reduction Creativity Innovation Culture Business Administration, Management, and Operations Li, Haidong Xu, Xiaoyun Zhao, Yaping Efficient Simulation Budget Allocation With Bound Information |
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This paper proposes a bound-based simulation budget allocation (BSBA) procedure for solving ranking and selection (R&S) problems in simulation optimization. For many practical applications, strict bounds on system performances can be obtained through empirical and theoretical approaches. These bounds provide additional information which may help solve R&S problems. In this paper, a new method of objective function estimation is proposed using both bound information and simulation outputs. This new estimation method is demonstrated to be particularly effective. To solve R&S problems, several asymptotic optimal allocation rules are also derived. Using these allocation rules, a BSBA procedure is proposed to achieve high efficiency in identifying the best design. Numerical experiments are provided to examine the performance of the proposed BSBA procedure. The computational results show that BSBA outperforms three compared allocation procedures, especially when bounds are tight or the simulation budget is small. |
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Li, Haidong Xu, Xiaoyun Zhao, Yaping |
author_facet |
Li, Haidong Xu, Xiaoyun Zhao, Yaping |
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Li, Haidong |
title |
Efficient Simulation Budget Allocation With Bound Information |
title_short |
Efficient Simulation Budget Allocation With Bound Information |
title_full |
Efficient Simulation Budget Allocation With Bound Information |
title_fullStr |
Efficient Simulation Budget Allocation With Bound Information |
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Efficient Simulation Budget Allocation With Bound Information |
title_sort |
efficient simulation budget allocation with bound information |
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Archīum Ateneo |
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2019 |
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https://archium.ateneo.edu/gsb-pubs/3 https://ieeexplore.ieee.org/abstract/document/8675337?casa_token=1B5V8SdZvwYAAAAA:azZh4w_1RqMdeCy4F7opiSxV0r8aSZiBhs_ZWdYuugSbDTe96OasEUlRSG2tqld5SQVTEkSbZbw |
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