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Offline Simulation Online Application: A New Framework of Simulation-Based Decision Making

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  • L. Jeff Hong

    (School of Data Science and School of Management, Fudan University, Shanghai 200433, P. R. China)

  • Guangxin Jiang

    (School of Management, Shanghai University, Shanghai 200444, P. R. China)

Abstract

Traditionally, simulation has been used as a tool of design to estimate, compare and optimize the performance of different system designs. It is rarely used in making real-time decisions due to the long computation delay of executing simulation models. However, with the fast growth of computing capability, we have observed more and more works on reusing simulation efforts for repeated experiments with the help of data analytics tools, and the target of these works is to solve real-time decision problems. In this paper, we distill the important features of these works and summarize a new simulation framework, called offline-simulation-online-application (OSOA) framework, which treats simulation as a data generator, applies state-of-the-art analytics tools to build predictive models, and then uses the predictive models for real-time applications. In this paper, we illustrate how to apply the OSOA framework on estimation, ranking and selection and simulation optimization, and provide a prospect of this new framework.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:apjorx:v:36:y:2019:i:06:n:s0217595919400153
    DOI: 10.1142/S0217595919400153
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    References listed on IDEAS

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    1. J B Jun & S H Jacobson & J R Swisher, 1999. "Application of discrete-event simulation in health care clinics: A survey," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(2), pages 109-123, February.
    2. Bruce Ankenman & Barry L. Nelson & Jeremy Staum, 2010. "Stochastic Kriging for Simulation Metamodeling," Operations Research, INFORMS, vol. 58(2), pages 371-382, April.
    3. Jun Luo & L. Jeff Hong & Barry L. Nelson & Yang Wu, 2015. "Fully Sequential Procedures for Large-Scale Ranking-and-Selection Problems in Parallel Computing Environments," Operations Research, INFORMS, vol. 63(5), pages 1177-1194, October.
    4. Haihui Shen & L. Jeff Hong & Xiaowei Zhang, 2018. "Enhancing stochastic kriging for queueing simulation with stylized models," IISE Transactions, Taylor & Francis Journals, vol. 50(11), pages 943-958, November.
    5. Dingeç, Kemal Dinçer & Hörmann, Wolfgang, 2012. "A general control variate method for option pricing under Lévy processes," European Journal of Operational Research, Elsevier, vol. 221(2), pages 368-377.
    6. Wei Xie & Barry L. Nelson & Russell R. Barton, 2014. "A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation," Operations Research, INFORMS, vol. 62(6), pages 1439-1452, December.
    7. Eunhye Song & Barry L. Nelson, 2015. "Quickly Assessing Contributions to Input Uncertainty," IISE Transactions, Taylor & Francis Journals, vol. 47(9), pages 893-909, September.
    8. Lucas, Andre & Klaassen, Pieter, 2006. "Discrete versus continuous state switching models for portfolio credit risk," Journal of Banking & Finance, Elsevier, vol. 30(1), pages 23-35, January.
    9. Carolina Osorio & Michel Bierlaire, 2013. "A Simulation-Based Optimization Framework for Urban Transportation Problems," Operations Research, INFORMS, vol. 61(6), pages 1333-1345, December.
    10. Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin, 1999. "Asymptotically Optimal Importance Sampling and Stratification for Pricing Path‐Dependent Options," Mathematical Finance, Wiley Blackwell, vol. 9(2), pages 117-152, April.
    11. Michael C. Fu, 2015. "Stochastic Gradient Estimation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 105-147, Springer.
    12. Ilya O. Ryzhov & Warren B. Powell & Peter I. Frazier, 2012. "The Knowledge Gradient Algorithm for a General Class of Online Learning Problems," Operations Research, INFORMS, vol. 60(1), pages 180-195, February.
    13. 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.
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