Journal
ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION
Volume 20, Issue 1, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/1667072.1667075
Keywords
Optimization via simulation; random search; ranking and selection
Funding
- National Science Foundation [DMI-0217690]
- Hong Kong Research Grants Council [CERG 613305, 613706]
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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 10(4) to 10(20) feasible solutions. These test cases represent response-surface models with known properties and realistic system simulation problems.
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