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