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

Jie Xu*, Barry L. Nelson, Jeff L. Hong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

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.

Original languageEnglish (US)
Article number3
JournalACM Transactions on Modeling and Computer Simulation
Volume20
Issue number1
DOIs
StatePublished - Jan 1 2010

Keywords

  • Optimization via simulation
  • Random search
  • Ranking and selection

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications

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