Monte Carlo Optimization of Stochastic Systems: Two New Approaches

摘要

The design of modern manufacturing systems presents a number of challenges. In particular, the stochastic nature of machine failures in combination with the large number of decision variables makes optimization of such systems difficult. This paper presents two new approaches to optimization of the complex stochastic systems that arise in a manufacturing context; both are Monte Carlo simulation-oriented, and are therefore broadly applicable. The first technique involves using a likelihood ratio gradient estimate to drive a Robbins-Monro algorithm, and is relevant to problems in which the decision variables are continuous. The second idea employs homotopy methods to follow an optimal path in decision variable space, and can be used for both discrete and continuous optimization.