Simulation Optimization and Artificial Intelligence

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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Publication Details

Journal
Foundations and Trends® in Optimization
Published
2025-12-04
DOI
https://doi.org/10.1561/2400000046
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
0.00
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article

Simulation Optimization and Artificial Intelligence

Foundations and Trends® in Optimization
Reinforcement Learning in Robotics
article

Simulation Optimization and Artificial Intelligence

article en

Abstract

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.

Foundations and Trends® in OptimizationVol. 9(4)
George Mason University (US), Peking University (CN), Smith Institute (GB)
Openalex Percentile: Top 19%
Reinforcement Learning in Robotics
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