A Relaxation of the Stochastic Ruler Method for Discrete Simulation Optimization

In this paper, we propose a relaxation to the stochastic ruler method originally described by Yan and Mukai in 1992 for asymptotically determining the global optima of discrete simulation optimization problems. We show that our proposed variant of the stochastic ruler method provides accelerated convergence to the optimal solution by providing computational results for two example problems, each of which support the better performance of the variant of the stochastic ruler over the original. We then provide the theoretical grounding for the asymptotic convergence in probability of the variant to the global optimal solution under the same set of assumptions as those underlying the original stochastic ruler method.