Abstract
Article history: Received June 10, 2014 Accepted October 22, 2014 Available online October 22 2014 Cell formation process is one of the first and the most important steps in designing cellular manufacturing systems. It consists of identifying part families according to the similarities in the design, shape, and presses of parts and dedicating machines to each part family based on the operations required by the parts. In this study, a hybrid method based on a combination of simulated annealing algorithm and dynamic programming was developed to solve a biobjective cell formation problem with duplicate machines. In the proposed hybrid method, each solution was represented as a permutation of parts, which is created by simulated annealing algorithm, and dynamic programming was used to partition this permutation into part families and determine the number of machines in each cell such that the total dissimilarity between the parts and the total machine investment cost are minimized. The performance of the algorithm was evaluated by performing numerical experiments in different sizes. Our computational experiments indicated that the results were very encouraging in terms of computational time and solution quality. Growing Science Ltd. All rights reserved. 5 © 201
Highlights
In today’s competitive market, manufacturing industries must be able to produce products with low production cost and high quality to deliver the products to customers on time
cellular manufacturing (CM) is a hybrid manufacturing system that joints the advantages of flow shops and job shops with characteristics such as reduced cycle times compared to jobs shops
As it has been assumed that the machine cells must be formed independently, the partition problem can be solved by dynamic programming algorithm
Summary
In today’s competitive market, manufacturing industries must be able to produce products with low production cost and high quality to deliver the products to customers on time. Chen and Srivastava (1994) formulated the CF problem as a quadratic programming model to maximize the total similarity between machines, subject to cell size limitation They employed SA to solve the problem and concluded that the performance of the SA is better than the graph-partitioning heuristic. Tavakkoli-Moghaddam et al (2008) modified the same work by considering reconfiguration and employed the SA algorithm to simultaneously minimize the inter-cell movements and machine costs. Their computational experiments showed that the gap between optimal and SA solutions is less than 4%. After setting the parameters of the proposed algorithm, its performance is evaluated by performing numerical experiments in different sizes
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