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Ordinal optimisation and simulation

Author

Listed:
  • Y-C Ho

    (Harvard University)

  • C G Cassandras

    (Boston University)

  • C-H Chen

    (University of Pennsylvania)

  • L Dai

    (Washington University)

Abstract

Simulation plays a vital role in designing and analysing stochastic systems, particularly, in comparing alternative system designs with a view to optimise system performance. Using simulation to analyse complex systems, however, can be both prohibitively expensive and time consuming. Efficiency is a key concern for the application of simulation to optimisation problems. Ordinal optimisation has emerged as an effective approach to significantly improve efficiency of simulation and optimisation. Ordinal optimisation for simulation problems achieves an exponential convergence rate. There are already several success stories of ordinal optimisation. This paper introduces the idea of ordinal optimisation, and reports some recent advances in this research. It also gives details of an extension of ordinal optimisation to a class of resource application problems.

Suggested Citation

  • Y-C Ho & C G Cassandras & C-H Chen & L Dai, 2000. "Ordinal optimisation and simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(4), pages 490-500, April.
    • Handle: RePEc:pal:jorsoc:v:51:y:2000:i:4:d:10.1057_palgrave.jors.2600906
    DOI: 10.1057/palgrave.jors.2600906
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    Citations

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    Cited by:

    1. Michael C. Fu, 2002. "Feature Article: Optimization for simulation: Theory vs. Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 192-215, August.
    2. H. S. Chang, 2005. "On the Probability of Correct Selection by Distributed Voting in Stochastic Optimization," Journal of Optimization Theory and Applications, Springer, vol. 125(1), pages 231-240, April.
    3. L. Jeff Hong & Barry L. Nelson, 2006. "Discrete Optimization via Simulation Using COMPASS," Operations Research, INFORMS, vol. 54(1), pages 115-129, February.
    4. A K Miranda & E Del Castillo, 2011. "Robust parameter design optimization of simulation experiments using stochastic perturbation methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 198-205, January.
    5. Sudip Bhattacharjee & Hong Zhang & R. Ramesh & Dee H. Andrews, 2007. "A Decomposition and Guided Simulation Methodology for Large-Scale System Design: A Study in QoS-Capable Intranets with Fixed and Mobile Components," INFORMS Journal on Computing, INFORMS, vol. 19(3), pages 429-442, August.
    6. Hyeong Soo Chang & Jiaqiao Hu, 2012. "On the Probability of Correct Selection in Ordinal Comparison over Dynamic Networks," Journal of Optimization Theory and Applications, Springer, vol. 155(2), pages 594-604, November.
    7. S.Y. Lin & Y.C. Ho, 2002. "Universal Alignment Probability Revisited," Journal of Optimization Theory and Applications, Springer, vol. 113(2), pages 399-407, May.
    8. Shing Chih Tsai & Tse Yang, 2017. "Rapid screening algorithms for stochastically constrained problems," Annals of Operations Research, Springer, vol. 254(1), pages 425-447, July.
    9. H. S. Chang, 2004. "Technical Note: On Ordinal Comparison of Policies in Markov Reward Processes," Journal of Optimization Theory and Applications, Springer, vol. 122(1), pages 207-217, July.
    10. Shing Chih Tsai, 2013. "Rapid Screening Procedures for Zero-One Optimization via Simulation," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 317-331, May.
    11. Zelda B. Zabinsky & David D. Linz, 2023. "Hesitant adaptive search with estimation and quantile adaptive search for global optimization with noise," Journal of Global Optimization, Springer, vol. 87(1), pages 31-55, September.
    12. Angun, M.E., 2004. "Black box simulation optimization : Generalized response surface methodology," Other publications TiSEM 2548e953-54ce-44e2-8c5b-7, Tilburg University, School of Economics and Management.
    13. Miguel Lejeune & François Margot, 2011. "Optimization for simulation: LAD accelerator," Annals of Operations Research, Springer, vol. 188(1), pages 285-305, August.

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