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Solving a class of simulation-based optimization problems using “optimality in probability”

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Abstract

We consider a class of simulation-based optimization problems using optimality in probability, an approach which yields what is termed a “champion solution”. Compared to the traditional optimality in expectation, this approach favors the solution whose actual performance is more likely better than that of any other solution; this is an alternative complementary approach to the traditional optimality sense, especially when facing a dynamic and nonstationary environment. Moreover, using optimality in probability is computationally promising for a class of simulation-based optimization problems, since it can reduce computational complexity by orders of magnitude compared to general simulation-based optimization methods using optimality in expectation. Accordingly, we have developed an “Omega Median Algorithm” in order to effectively obtain the champion solution and to fully utilize the efficiency of well-developed off-line algorithms to further facilitate timely decision making. An inventory control problem with nonstationary demand is included to illustrate and interpret the use of the Omega Median Algorithm, whose performance is tested using simulations.

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Acknowledgments

The authors’ work is supported in part by NSFC under grant U1733102, by CUHK(SZ) under grant PF.01.000404, by ATMRI under grant M4061216.057, by NTU under grant M58050030, by AcRF grant RG 33/10 M52050117, by NSF under grants CNS-1239021, ECCS-1509084, CNS-1645681, and IIP-1430145, by AFOSR under grant FA9550-15-1-0471, by ONR under grant N00014-09-1-1051, and by Bosch and the MathWorks.

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Authors and Affiliations

  1. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, China

    Jianfeng Mao

  2. Division of Systems Engineering, Boston University, Brookline, MA, 02446, USA

    Christos G. Cassandras

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  1. Jianfeng Mao
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  2. Christos G. Cassandras
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Correspondence to Jianfeng Mao.

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This article belongs to the Topical Collection: Special Issue on Performance Analysis and Optimization of Discrete Event Systems

Guest Editors: Christos G. Cassandras and Alessandro Giuas

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Mao, J., Cassandras, C.G. Solving a class of simulation-based optimization problems using “optimality in probability”. Discrete Event Dyn Syst 28, 35–61 (2018). https://doi.org/10.1007/s10626-017-0261-x

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  • DOI: https://doi.org/10.1007/s10626-017-0261-x

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