Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss * Corresponding author. how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.
W e propose an adaptive hyperbox algorithm (AHA), which is an instance of a locally convergent, random search algorithm for solving discrete optimization via simulation problems. Compared to the COMPASS algorithm, AHA is more efficient in high-dimensional problems. By analyzing models of the behavior of COM-PASS and AHA, we show why COMPASS slows down significantly as dimension increases, whereas AHA is less affected. Both AHA and COMPASS can be used as the local search algorithm within the Industrial Strength COMPASS framework, which consists of a global search phase, a local search phase, and a final cleanup phase. We compare the performance of AHA to COMPASS within the framework of Industrial Strength COMPASS and as stand-alone algorithms. Numerical experiments demonstrate that AHA scales up well in high-dimensional problems and has similar performance to COMPASS in low-dimensional problems.
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