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Efficient comparison of constrained systems using dormancy
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AI
DOI：10.1016/j.ejor.2012.08.012.png)
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Abstract
En 中文
We consider the problem of finding the best simulated system under a primary performance measure, while also satisfying stochastic constraints on secondary performance measures. We improve upon existing constrained selection procedures by allowing certain systems to become dormant, halting sampling for those systems as the procedure continues. A system goes dormant when it is found inferior to another system whose feasibility has not been determined, and returns to contention only if its superior system is eliminated. If found feasible, the superior system will eliminate the dormant system. By making systems dormant, we avoid collecting unnecessary observations from inferior systems. The paper also proposes other modifications, and studies the impact and benefits of our approaches (compared to similar constrained selection procedures) through experimental results and asymptotic approximations. Additionally, we discuss the difficulties associated with procedures that use sample means of unequal, random sample sizes, which commonly occurs within constrained selection and optimization-via-simulation. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Simulation
Ranking and selection
Stochastic constraints
Fully sequential algorithms
Multiple performance measures
Constrained selection
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W