A Sequential Budget Allocation Framework for Simulation Optimization

Abstract

Many problems in automation and manufacturing are most suitable to be modeled as simulation optimization problems. Solving these problems typically involves two efforts: one is to explore the solution space, and the other is to exploit the performance values of the sampled solutions. When the amount of computing budget is limited, we need to know how to balance these two efforts in order to obtain the best result. In this study, we derive two measures to quantify the marginal contribution of exploring the search space and exploiting the performance values. A sequential budget allocation framework is designed by keeping the two measures approximately the same at each iteration. Numerical experiments on both continuous and discrete simulation optimization problems demonstrate that our new approach can significantly enhance the computing efficiency.