A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
A Multiple Attribute Utility Theory Approach to Ranking and Selection
Management Science
Procedures for feasibility detection in the presence of multiple constraints
WSC '05 Proceedings of the 37th conference on Winter simulation
Finding the best in the presence of a stochastic constraint
WSC '05 Proceedings of the 37th conference on Winter simulation
Recent advances in ranking and selection
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Simulation optimization with hybrid golden region search
Winter Simulation Conference
A minimal switching procedure for constrained ranking and selection
Proceedings of the Winter Simulation Conference
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When selecting the best design of a system among a finite set of possible designs, there may be multiple selection criterion. One formulation of such a multi-criteria problem is minimization (or maximization) of one of the criterions while constraining the others. In this paper, we assume the criteria are unobservable mean values of stochastic outputs of simulation. We propose a new heuristic iterative algorithm for finding the best in this situation and use a number of experiments to demonstrate the performance of the algorithm.