A hybrid of shrinking ball method and optimal large deviation rate estimation in continuous contextual simulation optimization with single observation | Proceedings of the Winter Simulation Conference (2025)

research-article

Authors: Xiao Jin, Yichi Shen, Loo Hay Lee, Ek Peng Chew, Christine A. Shoemaker

WSC '20: Proceedings of the Winter Simulation Conference

Pages 2996 - 3007

Published: 13 May 2021 Publication History

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Abstract

We propose a new method for solving continuous contextual simulation optimization with a single observation. By adopting the estimation on the large deviation rate in the contextual ranking and selection problem, we transfer the old theorem to the continuous setting using a shrinking ball inspired construct. Through the estimation of the rate, the new method is expected to achieve the optimal performance in this new problem setting. Brief numerical experiments are conducted and show significant advantages of our method against the uniform sampling scheme.

References

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Gao, S., J. Du, and C.-H. Chen. 2019. "Selecting the Optimal System Design Under Covariates". In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 547--552. Institute of Electrical and Electronics Engineers, Inc.

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Gao, S., C. Li, and J. Du. 2019. "Rate Analysis for Offline Simulation Online Application". In Proceedings of the 2019 Winter Simulation Conference, edited by S. L.-M. M. R. C. S. P. H. N. Mustafee, K.-H.G. Bae and Y.-J. Son, 3468--3479. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.

Digital Library

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Glynn, P., and S. Juneja. 2004. "A Large Deviations Perspective on Ordinal Optimization". In Proceedings of the 2004 Winter Simulation Conference, edited by J. S. S. R. G. Ingalls, M. D. Rossetti and B. A. Peters, Volume 1, 585. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.

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Jin, X., H. Li, and L. H. Lee. 2019. "Optimal Budget Allocation in Simulation Analytics". In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 178--182. Institute of Electrical and Electronics Engineers, Inc.

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Kiatsupaibul, S., R. L. Smith, and Z. B. Zabinsky. 2018. "Single Observation Adaptive Search for Continuous Simulation Optimization". Operations Research 66(6):1713--1727.

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Linz, D. D., Z. B. Zabinsky, S. Kiatsupaibul, and R. L. Smith. 2017. "A Computational Comparison of Simulation Optimization Methods Using Single Observations within a Shrinking Ball on Noisy Black-box Functions with Mixed Integer and Continuous Domains". In Proceedings of the 2017 Winter Simulation Conference, edited by G. Z. N. M. G. W. W. K. V. Chan, A. D'Ambrogio and E. Page, 2045--2056. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.

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Nelson, B. L. 2016. "'Some Tactical Problems in Digital Simulation' for the Next 10 Years". Journal of Simulation 10(1):2--11.

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Shen, H., L. J. Hong, and X. Zhang. 2017. "Ranking and Selection with Covariates". In Proceedings of the 2017 Winter Simulation Conference, edited by G. Z. N. M. G. W. W. K. V. Chan, A. D'Ambrogio and E. Page, 2137--2148. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.

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Zhou, Li 2015. "A Survey on Contextual Multi-armed Bandits". https://arxiv.org/pdf/1508.03326.pdf, accessed 2015.08.03.

Cited By

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  • Shen YShoemaker C(2020)Global optimization for noisy expensive black-box multi-modal functions via radial basis function surrogateProceedings of the Winter Simulation Conference10.5555/3466184.3466531(3020-3031)Online publication date: 14-Dec-2020

    https://dl.acm.org/doi/10.5555/3466184.3466531

Index Terms

  1. A hybrid of shrinking ball method and optimal large deviation rate estimation in continuous contextual simulation optimization with single observation

    1. Computing methodologies

      1. Modeling and simulation

      2. Mathematics of computing

        1. Mathematical analysis

          1. Mathematical optimization

        2. Theory of computation

          1. Design and analysis of algorithms

            1. Mathematical optimization

        Index terms have been assigned to the content through auto-classification.

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        Published In

        A hybrid of shrinking ball method and optimal large deviation rate estimation in continuous contextual simulation optimization with single observation | Proceedings of the Winter Simulation Conference (6)

        WSC '20: Proceedings of the Winter Simulation Conference

        December 2020

        3329 pages

        ISBN:9781728194998

        Sponsors

        • SIGSIM: ACM Special Interest Group on Simulation and Modeling

        In-Cooperation

        • SCS: Society for Computer Simulation

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        IEEE Press

        Publication History

        Published: 13 May 2021

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        • Research-article

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        WSC '20

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        • SIGSIM

        WSC '20: Winter Simulation Conference

        December 14 - 18, 2020

        Florida, Orlando

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        Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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        A hybrid of shrinking ball method and optimal large deviation rate estimation in continuous contextual simulation optimization with single observation | Proceedings of the Winter Simulation Conference (7)

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        • Shen YShoemaker C(2020)Global optimization for noisy expensive black-box multi-modal functions via radial basis function surrogateProceedings of the Winter Simulation Conference10.5555/3466184.3466531(3020-3031)Online publication date: 14-Dec-2020

          https://dl.acm.org/doi/10.5555/3466184.3466531

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