Applications of Simulation-Optimization Methods in Environmental Policy Planning under Uncertainty

Abstract

Environmental policy formulation can prove especially complicated, since in general, system components contain considerable degrees of uncertainty. However, simulation-optimization (SO) techniques can be adapted to model a wide variety of problem types in which system components are stochastic. In this paper, it is shown how multiple environmental policy alternatives meeting required system criteria, or modelling-to-generate-alternatives (MGA), can be quickly and efficiently created using SO. The efficacy of this MGA approach is illustrated using two case studies. Furthermore, since SO techniques can be adapted to problems in which many system components are stochastic, the practicality of this approach can be extended into many other operational and strategic planning applications containing significant sources of uncertainty.