Handling stochastic constraints in discrete optimization via simulation

Abstract

We consider a discrete optimization via simulation problem with stochastic constraints on secondary performance measures where both objective and secondary performance measures need to be estimated by simulation. To solve the problem, we present a method called penalty function with memory (PFM), which determines a penalty value for a solution based on history of feasibility check on the solution. PFM converts a DOvS problem with stochastic constraints into a series of new optimization problems without stochastic constraints so that an existing DOvS algorithm can be applied to solve the new problem.