IDEAS home Printed from https://ideas.repec.org/a/wsi/apjorx/v33y2016i02ns0217595916500093.html
   My bibliography  Save this article

Ordinal Optimization with Computing Budget Allocation for Selecting an Optimal Subset

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0217595916500093
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0217595916500093?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. David W. Sullivan & James R. Wilson, 1989. "Restricted Subset Selection Procedures for Simulation," Operations Research, INFORMS, vol. 37(1), pages 52-71, February.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David Goldsman & Seong-Hee Kim & William S. Marshall & Barry L. Nelson, 2002. "Ranking and Selection for Steady-State Simulation: Procedures and Perspectives," INFORMS Journal on Computing, INFORMS, vol. 14(1), pages 2-19, February.
    2. Daniel Russo, 2020. "Simple Bayesian Algorithms for Best-Arm Identification," Operations Research, INFORMS, vol. 68(6), pages 1625-1647, November.
    3. Weiwei Fan & L. Jeff Hong & Barry L. Nelson, 2016. "Indifference-Zone-Free Selection of the Best," Operations Research, INFORMS, vol. 64(6), pages 1499-1514, December.
    4. Barry L. Nelson & Julie Swann & David Goldsman & Wheyming Song, 2001. "Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large," Operations Research, INFORMS, vol. 49(6), pages 950-963, December.
    5. Vasilii Erokhin & Li Diao & Tianming Gao & Jean-Vasile Andrei & Anna Ivolga & Yuhang Zong, 2021. "The Supply of Calories, Proteins, and Fats in Low-Income Countries: A Four-Decade Retrospective Study," IJERPH, MDPI, vol. 18(14), pages 1-30, July.
    6. Tianming Gao & Vasilii Erokhin, 2020. "Capturing a Complexity of Nutritional, Environmental, and Economic Impacts on Selected Health Parameters in the Russian High North," Sustainability, MDPI, vol. 12(5), pages 1-25, March.
    7. 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.
    8. Jiajun Xu & Suvrajeet Sen, 2024. "Ensemble Variance Reduction Methods for Stochastic Mixed-Integer Programming and their Application to the Stochastic Facility Location Problem," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 587-599, March.
    9. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Eric Bergerson & Megan Kurka & Ludek Kopacek, 2020. "Learning Demand Curves in B2B Pricing: A New Framework and Case Study," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1287-1306, May.
    10. Groves, Matthew & Branke, Juergen, 2019. "Top-κ selection with pairwise comparisons," European Journal of Operational Research, Elsevier, vol. 274(2), pages 615-626.
    11. Ahn, Dohyun, 2024. "Data-driven resource allocation for multi-target attainment," European Journal of Operational Research, Elsevier, vol. 318(3), pages 954-965.
    12. J. O. Royset & E. Y. Pee, 2012. "Rate of Convergence Analysis of Discretization and Smoothing Algorithms for Semiinfinite Minimax Problems," Journal of Optimization Theory and Applications, Springer, vol. 155(3), pages 855-882, December.
    13. David J. Eckman & Shane G. Henderson, 2022. "Posterior-Based Stopping Rules for Bayesian Ranking-and-Selection Procedures," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1711-1728, May.
    14. Ye Chen & Ilya O. Ryzhov, 2020. "Technical Note—Consistency Analysis of Sequential Learning Under Approximate Bayesian Inference," Operations Research, INFORMS, vol. 68(1), pages 295-307, January.
    15. 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.
    16. Qiushi Chen & Lei Zhao & Jan C. Fransoo & Zhe Li, 2019. "Dual-mode inventory management under a chance credit constraint," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(1), pages 147-178, March.
    17. Yoon, Moonyoung & Bekker, James, 2019. "Considering sample means in Rinott’s procedure with a Bayesian approach," European Journal of Operational Research, Elsevier, vol. 273(1), pages 249-258.
    18. Powell, Warren B., 2019. "A unified framework for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 275(3), pages 795-821.
    19. 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.
    20. Ye Chen & Ilya O. Ryzhov, 2023. "Balancing Optimal Large Deviations in Sequential Selection," Management Science, INFORMS, vol. 69(6), pages 3457-3473, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:apjorx:v:33:y:2016:i:02:n:s0217595916500093. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/apjor/apjor.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.