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In simulation-based optimization, the optimal setting of the input parametersof the objective function can be determined by heuristic optimizationtechniques. However, when simulators model the stochasticity of real-worldproblems, their output is a random variable and multiple evaluations of theobjective function are necessary to properly compare the expected performanceof different parameter settings. This paper presents a novel reactive samplesize algorithm based on parametric tests and indifference-zone selection, whichcan be used for improving the efficiency and robustness of heuristicoptimization methods. The algorithm reactively decides, in an online manner,the sample size to be used for each comparison during the optimizationaccording to observed statistical evidence. Tests employ benchmark functionsextended with artificial levels of noise and a simulation-based optimizationtool for hotel revenue management. Experimental results show that the reactivemethod can improve the efficiency and robustness of simulation-basedoptimization techniques.

Reactive Sample Size for Heuristic Search in Simulation-based Optimization