Production control in a network-failure prone manufacturing system with stochastic demand using improved response surface methodology

In this paper we consider the production control of a failure prone manufacturing network using the Hedging Point Policy (HPP). This system consist of a network of machines with relationship constraints that can be in one of four states: operational, in repair, starved and blocked. Broken machines are subject to a repair process, and up time and repair time in each phase for each machines is assumed to be exponentially distributed. The demand for the product produced by the final machine is assumed to be a Poisson process. Unmet demand is either backlogged or lost. The objective of this paper is to find the optimal production rates of each machine so as to minimize the long run average inventory and backlog cost. In order to solve this problem we use a simulation based optimization method that combines stochastic optimal control theory, discrete event simulation, experimental design and Automated Response Surface Methodology (RSM). We include a numerical example to illustrate the effectiveness of the proposed methodology.