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.

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.

In this monograph, cross-fertilization between simulation optimization and artificial intelligence is examined, with a particular focus on reinforcement learning, highlighting research that has been mutually beneficial. After reviewing relevant methodologies from stochastic simulation and optimization, an overview of foundational approaches of machine learning are provided, and then examples of the synergies between the fields are presented, followed by real-world applications and case studies, including some futurist and forward-looking concepts.