With the relentless increase in computing power and the ubiquitous availability of data in many industries, the fields of simulation optimization and artificial intelligence have emerged at the scientific and engineering forefront in their societal impact, manifested in the pervasiveness of technologies such as large language models, chatbots, digital twins, and agent-based systems. We examine cross-fertilization between simulation optimization and artificial intelligence, with a particular focus on reinforcement learning, highlighting research that has been mutually beneficial. After reviewing relevant methodologies from stochastic simulation and optimization, we provide an overview of foundational approaches of machine learning, and then present examples of the synergies between the fields, followed by real-world applications and case studies, including some futurist and forward-looking concepts.
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4 December 2025
Research Article|
December 04 2025
Simulation Optimization and Artificial Intelligence
Yijie Peng;
Yijie Peng
Department of Management Science and Information Systems,
Peking University
, China
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Chun-Hung Chen;
Chun-Hung Chen
Department of Systems Engineering and Operations Research,
George Mason University
, USA
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Michael C. Fu
Michael C. Fu
Robert H. Smith School of Business and Institute for Systems Research, University of Maryland
, USA
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Online ISSN: 2167-3918
Print ISSN: 2167-3888
© 2025 Y. Peng et al.
2025
Y. Peng et al.
Licensed re-use rights only
Foundations and Trends in Optimization (2025) 9 (4): 407–566.
Citation
Peng Y, Chen C, Fu MC (2025), "Simulation Optimization and Artificial Intelligence". Foundations and Trends in Optimization, Vol. 9 No. 4 pp. 407–566, doi: https://doi.org/10.1561/2400000046
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