Discrete Optimization via Simulation

L. Jeff Hong (), Barry L. Nelson () and Jie Xu ()
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L. Jeff Hong: City University of Hong Kong
Barry L. Nelson: Northwestern University
Jie Xu: George Mason University

Chapter Chapter 2 in Handbook of Simulation Optimization, 2015, pp 9-44 from Springer

Abstract: Abstract 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.

Keywords: Ordinal Optimization (OO); Model Reference Adaptive Search (MRAS); Optimal Computing Budget Allocation (OCBA); Nested Partitions Algorithm; Local Optimal Solution (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (19)

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DOI: 10.1007/978-1-4939-1384-8_2

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