IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v271y2018i2p733-745.html
   My bibliography  Save this article

Self-adjusting the tolerance level in a fully sequential feasibility check procedure

Author

Listed:
  • Lee, Mi Lim
  • Park, Chuljin
  • Park, Dong Uk

Abstract

We consider the problem of determining the feasibility of systems when the performance measures in stochastic constraints need to be evaluated via simulation. We develop a new procedure, namely the adaptive feasibility check procedure. Specifically, the procedure uses an existing feasibility check procedure iteratively as its subroutine with a decreasing sequence of tolerance levels. Our procedure is designed to return the set of strictly feasible systems with at least a prespecified probability. The validity and efficiency of the procedure are investigated through both analytical and experimental results. The procedure is also tested using numerical examples.

Suggested Citation

  • Lee, Mi Lim & Park, Chuljin & Park, Dong Uk, 2018. "Self-adjusting the tolerance level in a fully sequential feasibility check procedure," European Journal of Operational Research, Elsevier, vol. 271(2), pages 733-745.
    • Handle: RePEc:eee:ejores:v:271:y:2018:i:2:p:733-745
    DOI: 10.1016/j.ejor.2018.05.045
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221718304569
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2018.05.045?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. Chuljin Park & Seong-Hee Kim, 2015. "Penalty Function with Memory for Discrete Optimization via Simulation with Stochastic Constraints," Operations Research, INFORMS, vol. 63(5), pages 1195-1212, October.
    2. Tsai, Shing Chih & Fu, Sheng Yang, 2014. "Genetic-algorithm-based simulation optimization considering a single stochastic constraint," European Journal of Operational Research, Elsevier, vol. 236(1), pages 113-125.
    3. Ahmed, Mohamed A. & Alkhamis, Talal M., 2009. "Simulation optimization for an emergency department healthcare unit in Kuwait," European Journal of Operational Research, Elsevier, vol. 198(3), pages 936-942, November.
    4. 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.
    5. Sigrún Andradóttir & Seong‐Hee Kim, 2010. "Fully sequential procedures for comparing constrained systems via simulation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(5), pages 403-421, August.
    6. Susan R. Hunter & Raghu Pasupathy, 2013. "Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets," INFORMS Journal on Computing, INFORMS, vol. 25(3), pages 527-542, August.
    7. Peter I. Frazier, 2014. "A Fully Sequential Elimination Procedure for Indifference-Zone Ranking and Selection with Tight Bounds on Probability of Correct Selection," Operations Research, INFORMS, vol. 62(4), pages 926-942, August.
    8. Healey, Christopher M. & Andradóttir, Sigrún & Kim, Seong-Hee, 2013. "Efficient comparison of constrained systems using dormancy," European Journal of Operational Research, Elsevier, vol. 224(2), pages 340-352.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cheng, Zhenxia & Luo, Jun & Wu, Ruijing, 2023. "On the finite-sample statistical validity of adaptive fully sequential procedures," European Journal of Operational Research, Elsevier, vol. 307(1), pages 266-278.
    2. Yuwei Zhou & Sigrún Andradóttir & Seong-Hee Kim & Chuljin Park, 2022. "Finding Feasible Systems for Subjective Constraints Using Recycled Observations," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3080-3095, November.

    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. Yuwei Zhou & Sigrún Andradóttir & Seong-Hee Kim & Chuljin Park, 2022. "Finding Feasible Systems for Subjective Constraints Using Recycled Observations," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3080-3095, November.
    2. L. Jeff Hong & Jun Luo & Barry L. Nelson, 2015. "Chance Constrained Selection of the Best," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 317-334, May.
    3. 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.
    4. Daniel Russo, 2020. "Simple Bayesian Algorithms for Best-Arm Identification," Operations Research, INFORMS, vol. 68(6), pages 1625-1647, November.
    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.
    6. 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.
    7. Juergen Branke & Wen Zhang, 2019. "Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 831-865, September.
    8. Tsai, Shing Chih & Yeh, Yingchieh & Kuo, Chen Yun, 2021. "Efficient optimization algorithms for surgical scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 293(2), pages 579-593.
    9. Weiwei Fan & L. Jeff Hong & Xiaowei Zhang, 2020. "Distributionally Robust Selection of the Best," Management Science, INFORMS, vol. 66(1), pages 190-208, January.
    10. Hainan Guo & Haobin Gu & Yu Zhou & Jiaxuan Peng, 2022. "A data-driven multi-fidelity simulation optimization for medical staff configuration at an emergency department in Hong Kong," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 238-262, June.
    11. Demet Batur & Lina Wang & F. Fred Choobineh, 2018. "Methods for System Selection Based on Sequential Mean–Variance Analysis," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 724-738, November.
    12. Ye Chen & Ilya O. Ryzhov, 2023. "Balancing Optimal Large Deviations in Sequential Selection," Management Science, INFORMS, vol. 69(6), pages 3457-3473, June.
    13. Haihui Shen & L. Jeff Hong & Xiaowei Zhang, 2021. "Ranking and Selection with Covariates for Personalized Decision Making," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1500-1519, October.
    14. Chuljin Park & Seong-Hee Kim, 2015. "Penalty Function with Memory for Discrete Optimization via Simulation with Stochastic Constraints," Operations Research, INFORMS, vol. 63(5), pages 1195-1212, October.
    15. Hyeong Soo Chang, 2020. "An asymptotically optimal strategy for constrained multi-armed bandit problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 91(3), pages 545-557, June.
    16. Demet Batur & F. Fred Choobineh, 2021. "Selecting the Best Alternative Based on Its Quantile," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 657-671, May.
    17. Ellen Bockstal & Broos Maenhout, 2019. "A study on the impact of prioritising emergency department arrivals on the patient waiting time," Health Care Management Science, Springer, vol. 22(4), pages 589-614, December.
    18. Niyirora, Jerome & Zhuang, Jun, 2017. "Fluid approximations and control of queues in emergency departments," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1110-1124.
    19. James T. Lin & Chun-Chih Chiu, 2018. "A hybrid particle swarm optimization with local search for stochastic resource allocation problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 481-495, March.
    20. Man Yiu Tsang & Tony Sit & Hoi Ying Wong, 2022. "Adaptive Robust Online Portfolio Selection," Papers 2206.01064, arXiv.org.

    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:eee:ejores:v:271:y:2018:i:2:p:733-745. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.