Ordinal Comparison via the Nested Partitions Method

We analyze a new approach for simulation-based optimization of discrete event systems that draws on two recent stochastic optimization methods: an adaptive sampling approach called the nested partitions method and ordinal optimization. The ordinal optimization perspectives provides new insights into the convergence of the nested partitions method and guidelines for its implementation. We also use this approach to show that global convergence requires relatively simulation runs and propose new effective variants of the algorithm. Simulation results are presented to demonstrate the key results.