Simulation Budget Allocation for Further Enhancing the Efficiency of Ordinal Optimization

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 determines a highly efficient number of simulation replications or samples and significantly reduces the total simulation cost. We also compare several different allocation procedures, including a popular two-stage procedure in simulation literature. Numerical testing shows that our approach is much more efficient than all compared methods. The results further indicate that our approach can obtain a speedup factor of higher than 20 above and beyond the speedup achieved by the use of ordinal optimization for a 210-design example. + Corresponding author: Professor Chun-Hung Chen, Dept. of Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104-6315, Fax: 215-898-5020; Tel: 215-898-3967, Email:[email protected]. ++ This work has been supported in part by NSF under Grant DMI-9732173, by the U.S. Department of Transportation under a grant from the University Transportation Centers Program through the Mid-Atlantic Transportation Consortium, by Sandia National Laboratories under Contract BD-0618, and by the University of Pennsylvania Research Foundation.