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A New Budget Allocation Framework for the Expected Opportunity Cost

Author

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  • Siyang Gao

    (Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong)

  • Weiwei Chen

    (Department of Supply Chain Management, Rutgers University, Newark, New Jersey 07102)

  • Leyuan Shi

    (Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, Wisconsin 53706; Department of Industrial Engineering and Management, Peking University, Beijing 100087, China)

Abstract

In this paper, we present a new budget allocation framework for the problem of selecting the best simulated design from a finite set of alternatives. The new framework is developed on the basis of general underlying distributions and a finite simulation budget. It adopts the expected opportunity cost (EOC) quality measure, which, compared to the traditional probability of correct selection (PCS) measure, penalizes a particularly bad choice more than a slightly incorrect selection, and is thus preferred by risk-neutral practitioners and decision makers. To this end, we establish a closed-form approximation of EOC to formulate the budget allocation problem and derive the corresponding optimality conditions. A sequential budget allocation algorithm is then developed for implementation. The efficiency of the proposed method is illustrated via numerical experiments. We also link the EOC and PCS-based budget allocation problems by showing that the two are asymptotically equivalent. This result explains, to some extent, the similarity in performance between the EOC and PCS allocation procedures observed in the literature.

Suggested Citation

  • Siyang Gao & Weiwei Chen & Leyuan Shi, 2017. "A New Budget Allocation Framework for the Expected Opportunity Cost," Operations Research, INFORMS, vol. 65(3), pages 787-803, June.
    • Handle: RePEc:inm:oropre:v:65:y:2017:i:3:p:787-803
    DOI: 10.1287/opre.2016.1581
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    References listed on IDEAS

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    Cited by:

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    4. Zhongshun Shi & Siyang Gao & Hui Xiao & Weiwei Chen, 2019. "A worst‐case formulation for constrained ranking and selection with input uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(8), pages 648-662, December.
    5. Zhongshun Shi & Yijie Peng & Leyuan Shi & Chun-Hung Chen & Michael C. Fu, 2022. "Dynamic Sampling Allocation Under Finite Simulation Budget for Feasibility Determination," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 557-568, January.
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    7. Weiwei Chen & Siyang Gao & Wenjie Chen & Jianzhong Du, 2023. "Optimizing resource allocation in service systems via simulation: A Bayesian formulation," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 65-81, January.

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