Designing Cellular Manufacturing Systems under Uncertainty

This paper addresses a stochastic model in order to design the cellular manufacturing systems (CMSs) under uncertain environment. In real-world cases, any parameters such as demand, processing time, inter arrival time and etc may change over the planning horizon. In this research, it’s assumed that processing time for parts on machines and arrival time for parts to cells are stochastic and described by exponential distribution which yield more flexibility for the model and the results. In uncertain environments an approach which can analyze this problem is queuing theory. In this paper, we assume that each machine is a server and each part is a customer where servers should service to customers. Therefore, we have a queue system which can be optimized by queue theory where this approach is not researched well in the literature. The aim of this model is to minimize summation of three cost types: (1) the idleness costs for machines which introduced as servers, (2) total cost of sub-contracting for exceptional elements and (3) the cost of resource underutilization. Finally, some numerical examples are illustrated to show effectiveness of the proposed approach. Also, sensitivity analysis will be performed to learn more about behavior of the model. © 2009 World Academic Press, UK. All rights reserved.