This chapter describes tools and techniques that are useful for optimization via simulation—maximizing or minimizing the expected value of a performance measure of a stochastic simulation—when the decision variables are discrete. Ranking and selection, globally and locally convergent random search and ordinal optimization are covered, along with a collection of “enhancements” that may be applied to many different discrete optimization via simulation algorithms. We also provide strategies for using commercial solvers.
CITATION STYLE
Hong, L. J., Nelson, B. L., & Xu, J. (2015). Discrete optimization via simulation. In International Series in Operations Research and Management Science (Vol. 216, pp. 9–44). Springer New York LLC. https://doi.org/10.1007/978-1-4939-1384-8_2
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