Automatic generation of efficient policy alternatives via simulation-optimization

Simulation-optimization methods can be used for many practical and industrial problems in which some or all of the system components are stochastic. These techniques can be applied to a wide variety of problem types, including those in which some functions cannot be represented analytically. In contrast to earlier function optimization approaches, in this paper, these techniques are used for generating several new policy options for planning applications. By using this approach, multiple policy alternatives can be created that meet established system criteria, while simultaneously remaining acceptable and implementable in practice. A subsequent comparative evaluation of the alternatives would be undertaken prior to final policy selection. An illustrative application of the method is provided to demonstrate the usefulness of this approach in the planning phase of policy design.