Affiliations: Department of Industrial Engineering, Faculty of
Engineering, University of Tehran, Tehran, Iran | Department of Industrial Engineering, Iran University
of Science and Technology, Tehran, Iran | Department of Industrial Engineering, Dalhousie
University, Halifax, Nova Scotia, B3J 2X4 Canada
Abstract: This paper presents a fuzzy linear mix-integer programming model for
design of cellular manufacturing systems with fuzzy part demands and product
mix changeable under a multi-period planning horizon. In this dynamic
condition, the best cell design for one period may not be efficient for
subsequent periods and the reconfiguration of cells is required. The proposed
model can determine the production volume for each part considering its fuzzy
demand. The other advantages of the proposed model are as follows: considering
inter-cell material handling with constant batch size, alternative process plan
for part types, operation sequence, machine relocation, machine replication,
machine utilization and cell number flexibility. Main constraints are the cell
size, machine capacity and production volume limitations. The objective is to
minimize the sum of the constant/variable/relocation machine costs as well as
inter-cell movements cost. Because of the complexity of the proposed model,
which is a combinatorial nonlinear optimization, we develop an efficient
genetic algorithm with novel representation and operators for solving the
proposed model. 29 small, medium and large-sized problems are generated to
evaluate the performance of the proposed model and the efficiency of the
developed genetic algorithm.
Keywords: Cellular manufacturing systems (CMS), fuzzy, genetic algorithms
