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Discrete Optimization via Simulation

In: Handbook of Simulation Optimization

Author

Listed:
  • L. Jeff Hong

    (City University of Hong Kong)

  • Barry L. Nelson

    (Northwestern University)

  • Jie Xu

    (George Mason University)

Abstract

This chapter describes tools and techniques that are useful for optimization via simulation—maximizing or minimizing the expected value of a performance measure of a stochastic simulation—when the decision variables are discrete. Ranking and selection, globally and locally convergent random search and ordinal optimization are covered, along with a collection of “enhancements” that may be applied to many different discrete optimization via simulation algorithms. We also provide strategies for using commercial solvers.

Suggested Citation

  • L. Jeff Hong & Barry L. Nelson & Jie Xu, 2015. "Discrete Optimization via Simulation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 9-44, Springer.
    • Handle: RePEc:spr:isochp:978-1-4939-1384-8_2
    DOI: 10.1007/978-1-4939-1384-8_2
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    Citations

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    Cited by:

    1. Ying Zhong & Shaoxuan Liu & Jun Luo & L. Jeff Hong, 2022. "Speeding Up Paulson’s Procedure for Large-Scale Problems Using Parallel Computing," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 586-606, January.
    2. Snoeck, André & Winkenbach, Matthias & Fransoo, Jan C., 2023. "On-demand last-mile distribution network design with omnichannel inventory," Other publications TiSEM 83b06c9f-2a65-4aaf-880b-2, Tilburg University, School of Economics and Management.
    3. Zuzana Nedělková & Peter Lindroth & Michael Patriksson & Ann-Brith Strömberg, 2018. "Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space," Annals of Operations Research, Springer, vol. 265(1), pages 93-118, June.
    4. Kyle Cooper & Susan R. Hunter, 2020. "PyMOSO: Software for Multiobjective Simulation Optimization with R-PERLE and R-MinRLE," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1101-1108, October.
    5. Snoeck, André & Winkenbach, Matthias & Fransoo, Jan C., 2023. "On-demand last-mile distribution network design with omnichannel inventory," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    6. Preil, Deniz & Krapp, Michael, 2022. "Bandit-based inventory optimisation: Reinforcement learning in multi-echelon supply chains," International Journal of Production Economics, Elsevier, vol. 252(C).
    7. Zhou, Tianli & Fields, Evan & Osorio, Carolina, 2023. "A data-driven discrete simulation-based optimization algorithm for car-sharing service design," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    8. 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.
    9. Deniz Preil & Michael Krapp, 2023. "Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation," Papers 2302.07695, arXiv.org.
    10. Daniel Russo, 2020. "Simple Bayesian Algorithms for Best-Arm Identification," Operations Research, INFORMS, vol. 68(6), pages 1625-1647, November.
    11. 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.
    12. 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.
    13. Flötteröd, Gunnar, 2017. "A search acceleration method for optimization problems with transport simulation constraints," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 239-260.
    14. Jalali, Hamed & Van Nieuwenhuyse, Inneke & Picheny, Victor, 2017. "Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise," European Journal of Operational Research, Elsevier, vol. 261(1), pages 279-301.
    15. Yifan Zhou & Chao Yuan & Tian Ran Lin & Lin Ma, 2021. "Maintenance policy structure investigation and optimisation of a complex production system with intermediate buffers," Journal of Risk and Reliability, , vol. 235(3), pages 458-473, June.
    16. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    17. Javad Seif & Mohammad Dehghanimohammadabadi & Andrew Junfang Yu, 2020. "Integrated preventive maintenance and flow shop scheduling under uncertainty," Flexible Services and Manufacturing Journal, Springer, vol. 32(4), pages 852-887, December.
    18. L. Jeff Hong & Guangxin Jiang, 2019. "Offline Simulation Online Application: A New Framework of Simulation-Based Decision Making," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(06), pages 1-22, December.

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