Convex Optimization
A large deviations perspective on ordinal optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
A testbed of simulation-optimization problems
Proceedings of the 38th conference on Winter simulation
SimOpt: a library of simulation optimization problems
Proceedings of the Winter Simulation Conference
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Consider the context of constrained simulation optimization (SO), that is, optimization problems where the objective function and constraints are known through a Monte Carlo simulation, with corresponding estimators possibly dependent. We identify the nature of sampling plans that characterize efficient algorithms, particularly in large countable spaces. We show that in a certain asymptotic sense, the optimal sampling characterization, that is, the sampling budget for each system that guarantees optimal convergence rates, depends on a single easily estimable quantity called the score. This result provides a useful and easily implementable sampling allocation that approximates the optimal allocation, which is otherwise intractable due to it being the solution to a difficult bilevel optimization problem. Our results point to a simple sequential algorithm for efficiently solving large-scale constrained simulation optimization problems on finite sets.