Industrial strength COMPASS : A comprehensive algorithm and software for optimization via simulation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Detail(s)

Original languageEnglish
Article number3
Journal / PublicationACM Transactions on Modeling and Computer Simulation
Volume20
Issue number1
Publication statusPublished - 1 Jan 2010
Externally publishedYes

Abstract

Industrial Strength COMPASS (ISC) is a particular implementation of a general framework for optimizing the expected value of a performance measure of a stochastic simulation with respect to integer-ordered decision variables in a finite (but typically large) feasible region defined by linear-integer constraints. The framework consists of a global-search phase, followed by a local-search phase, and ending with a clean-up (selection of the best) phase. Each phase provides a probability 1 convergence guarantee as the simulation effort increases without bound: Convergence to a globally optimal solution in the global-search phase; convergence to a locally optimal solution in the local-search phase; and convergence to the best of a small number of good solutions in the clean-up phase. In practice, ISC stops short of such convergence by applying an improvement-based transition rule from the global phase to the local phase; a statistical test of convergence from the local phase to the clean-up phase; and a ranking-and-selection procedure to terminate the clean-up phase. Small-sample validity of the statistical test and ranking-and-selection procedure is proven for normally distributed data. ISC is compared to the commercial optimization via simulation package OptQuest on five test problems that range from 2 to 20 decision variables and on the order of 104 to 1020 feasible solutions. These test cases represent response-surface models with known properties and realistic system simulation problems. © 2010 ACM.

Research Area(s)

  • Optimization via simulation, Random search, Ranking and selection