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Distributed simulation: state-of-the-art and potential for operational research

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  • Taylor, Simon J.E.

Abstract

In Operational Research conventional simulation practices typically focus on the conceptualization, development and use of a single model simulated on a single computer by a single analyst. Since the late 1970s the field of Distributed Simulation has led research into how to speed up simulation and how to compose large-scale simulations consisting of many reusable models running using distributed computers. There have been significant advances in the theories and technologies underpinning Distributed Simulation and there have been major successes in defence, computer systems design and smart urban environments. However, from an Operational Research perspective, Distributed Simulation has had little impact on mainstream research and practice. To argue the potential benefits of Distributed Simulation for Operational Research, this article gives an overview of Distributed Simulation approaches and technologies as well as discussing the state-of-the-art of Distributed Simulation applications. It will investigate the potential advantages of Distributed Simulation for Operational Research and present a possible sustainable future, based on experiences from e-Science, that will help Operational Research meet future challenges such as those emerging from Big Data Analytics, Cyber-physical systems, Industry 4.0, Digital Twins and Smart environments.

Suggested Citation

  • Taylor, Simon J.E., 2019. "Distributed simulation: state-of-the-art and potential for operational research," European Journal of Operational Research, Elsevier, vol. 273(1), pages 1-19.
  • Handle: RePEc:eee:ejores:v:273:y:2019:i:1:p:1-19
    DOI: 10.1016/j.ejor.2018.04.032
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    Cited by:

    1. Chiang, Nai-Yuan & Lin, Yiqing & Long, Quan, 2020. "Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods," Operations Research Perspectives, Elsevier, vol. 7(C).
    2. Swan, Jerry & Adriaensen, Steven & Brownlee, Alexander E.I. & Hammond, Kevin & Johnson, Colin G. & Kheiri, Ahmed & Krawiec, Faustyna & Merelo, J.J. & Minku, Leandro L. & Özcan, Ender & Pappa, Gisele L, 2022. "Metaheuristics “In the Large”," European Journal of Operational Research, Elsevier, vol. 297(2), pages 393-406.
    3. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    4. Simon Gorecki & Jalal Possik & Gregory Zacharewicz & Yves Ducq & Nicolas Perry, 2020. "A Multicomponent Distributed Framework for Smart Production System Modeling and Simulation," Sustainability, MDPI, vol. 12(17), pages 1-26, August.

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