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Screening and selection procedures with control variates and correlation induction techniques
Author(s) -
Tsai Shing Chih,
Kuo Chen Hao
Publication year - 2012
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/nav.21491
Subject(s) - variance reduction , control variates , selection (genetic algorithm) , latin hypercube sampling , ranking (information retrieval) , computer science , variance (accounting) , sampling (signal processing) , control (management) , rank (graph theory) , statistics , mathematics , monte carlo method , machine learning , artificial intelligence , markov chain monte carlo , economics , hybrid monte carlo , accounting , filter (signal processing) , combinatorics , computer vision
Abstract We consider the problem of identifying the simulated system with the best expected performance measure when the number of alternatives is finite and small (often < 500). Recently, more research efforts in the simulation community have been directed to develop ranking and selection (R&S) procedures capable of exploiting variance reduction techniques (especially the control variates). In this article, we propose new R&S procedures that can jointly use control variates and correlation induction techniques (including antithetic variates and Latin hypercube sampling). Empirical results and a realistic illustration show that the proposed procedures outperform the conventional procedures using sample means or control variates alone. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012