Computing efforts allocation for ordinal optimization and discrete event simulation

Abstract

Ordinal optimization has emerged as an efficient technique for simulation and optimization. Exponential convergence rates can be achieved in many cases. In this paper, we present a new approach that can further enhance the efficiency of ordinal optimization. Our approach intelligently determines the optimal number of simulation replications (or samples) and significantly reduces the total simulation cost. Numerical illustrations are included. The results indicate that our approach can obtain an additional 74% computation time reduction above and beyond the reduction obtained through the use of ordinal optimization for a 10-design example.