Stochastic models for performance analysis of multistate flexible manufacturing cells

Abstract

Performance analysis of flexible manufacturing cells (FMCs) can help companies find the pros and cons of production processes. However, the emphasis has been on issues like cell formation, layout design and scheduling optimization. Little seems to have been done to assess the reliability of an FMC. In this paper, we develop the stochastic models for the performance analysis mainly on the reliability of two different FMCs configured from a set of teaching intelligent flexible manufacturing system (TIFMS). The closed form solutions of probabilities of system states are obtained. Then, utilization rate of equipment in the cell and productivities of the two FMCs as the performance indexes are calculated and optimized. Compared to simulation methods, the closed form solutions make calculations of the performance indexes faster and more accurate. When random variables in the stochastic models are assumed to follow non-exponential distributions, the effects of them on the performance indexes are discussed. The objective of this paper is to fill up the gap that the closed form solutions are difficult to obtain as the number of machine tool increases. Another objective is to optimize the performance indexes to help engineers better evaluate the performance of FMC. Numerical analysis cases are used to illustrate the proposed stochastic models.