Stochastic optimization applied to a manufacturing system operation problem

This paper deals with stochastic optimization of a discrete-event simulation model for the solution of a manufacturing system operation problem. Gradient estimates are obtained by the application of the infinitesimal perturbation analysis (IPA) technique. We begin with background material on stochastic approximation (SA) and the IPA technique, their potential value in finding optimal solutions to manufacturing system operation problems, and limitations concerning their applicability. Next we present our attempt to solve a real problem (the design of a partially-automated assembly line in an electronics manufacturing facility) using this approach. A sequence of models is described moving from one which embodies some restrictive assumptions through to models which more closely approximate the real system. All of the models are implemented in the SIMAN IV simulation language incorporating user-written code (written in C++) implementing the SA and IPA algorithms. We report and interpret the results obtained with the different models and close with concluding remarks on the current value of this technique in solving this kind of system design problem.