Stratified filtered sampling in stochastic optimization

We develop a methodology for evaluating a decision strategy generated by a stochastic optimization model. The methodology is based on a pilot study in which we estimate the distribution of performance associated with the strategy, and define an appropriate stratified sampling plan. An algorithm we call filtered search allows us to implement this plan efficiently. We demonstrate the approach's advantages with a problem in asset/liability management for an insurance company.