USING STOCHASTIC OPTIMIZATION TO DETERMINE THRESHOLD VALUES FOR THE CONTROL OF UNRELIABLE MANUFACTURING SYSTEMS

A manufacturing system with two tandem machines producing one part typ e is considered in this work. The machines are unreliable, each having two states, up and down. Both surplus controls and Kanban systems are considered. Algorithms for approximating the optimal threshold values are developed. First, perturbation analysis techniques are employed t o obtain consistent gradient estimates based on a single simulation ru n. Then, iterative algorithms of the stochastic optimization type are constructed. It is shown that the algorithms converge to the optimal t hreshold values in an appropriate sense. Numerical examples are provid ed to demonstrate the performance of the algorithms.