Ranking and Selection: Efficient Simulation Budget Allocation

Chun-Hung Chen (), Stephen E. Chick (), Loo Hay Lee () and Nugroho A. Pujowidianto ()
Additional contact information
Chun-Hung Chen: George Mason University
Stephen E. Chick: INSEAD
Loo Hay Lee: National University of Singapore
Nugroho A. Pujowidianto: Hewlett-Packard Singapore

Chapter Chapter 3 in Handbook of Simulation Optimization, 2015, pp 45-80 from Springer

Abstract: Abstract This chapter reviews the problem of selecting the best of a finite set of alternatives, where best is defined with respect to the highest mean performance, and where the performance is uncertain but may be estimated with simulation. This problem has been explored from several perspectives, including statistical ranking and selection, multiple comparisons, and stochastic optimization. Approaches taken in the literature include frequentist statistics, Bayesian statistics, related heuristics, and asymptotic convergence in probability. This chapter presents algorithms that are derived from Bayesian and related conceptual frameworks to provide empirically effective performance for the ranking and selection problem. In particular, we motivate the optimal computing budget allocation (OCBA) algorithm and expected value of information (EVI) approaches, give example algorithms, and provide pointers to the literature for detailed derivations and extensions of these approaches.

Keywords: Allocation Rule; Correct Selection; Multiple Alternative; Linear Loss; Equal Allocation (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (13)

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DOI: 10.1007/978-1-4939-1384-8_3

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