The evolution of cell formation problem methodologies based on recent studies (1997–2008): Review and directions for future research

A hybrid grouping genetic algorithm for the cell formation problem

The cell formation problem determines the decomposition of the manufacturing cells of a production system in which machines are assigned to these cells to process one or more part families so that each cell is operated independently and the intercellular movements are minimized or the number of parts flow processed within cells is maximized. In this study, a simple yet effective simulated annealing-based approach, SACF, is proposed to solve the cell formation problem. Considerable efforts are devoted to the design of parts and machine assignment procedures to direct SACF to converge to solutions with good values of grouping efficacy. A set of 25 test problems with various sizes drawn from the literature is used to test the performance of the proposed heuristic algorithm. The corresponding results are compared to several well-known algorithms published. The comparative study shows that the proposed SACF algorithm improves the grouping efficacy for 72% of the test problems. The proposed algorithm should thus be useful to both practitioners and researchers.

https://ir.nctu.edu.tw:443/bitstream/11536/9500/1/000253183700003.pdf

A simulated annealing algorithm for manufacturing cell formation problems

Article history: Received 1 April 2010 Received in revised form 22 July 2010 Accepted 30 July 2010 Available online 1 Auguest 2010 Meta-heuristic approaches are general algorithmic framework, often nature-inspired and designed to solve NP-complete optimization problems in cellular manufacturing systems and has been a growing research area for the past two decades. This paper discusses various metaheuristic techniques such as evolutionary approach, Ant colony optimization, simulated annealing, Tabu search and other recent approaches, and their applications to the vicinity of group technology/cell formation (GT/CF) problem in cellular manufacturing. The nobility of this paper is to incorporate various prevailing issues, open problems of meta-heuristic approaches, its usage, comparison, hybridization and its scope of future research in the aforesaid area. © 2010 Growing Science Ltd. All rights reserved.

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Meta-heuristics in cellular manufacturing: A state-of-the-art review

An efficient approach to determine cell formation, cell layout and intracellular machine sequence in cellular manufacturing systems

The cell formation problem determines the decomposition of the manufacturing cells of a production system in which machines are assigned to these cells to process one or more part families so that each cell is operated independently and the intercellular flows are minimised or the number of parts flow processed within cells is maximised. In this paper, a tabu search heuristic—TSCF—that consists of dynamic tabu tenure with a long-term memory mechanism is presented to solve the cell formation problem. Test problems adopted from the literature and generated randomly are used to evaluate the performance of the proposed algorithm. In addition, two methods for quickly generating the initial solutions are proposed, namely the group-and-assign (GAA) method, and the random approach. Computational results indicate that the GAA method, accompanied by the TSCF algorithm can produce optimal solutions in less than or equal to 0.005 s for all small- and medium-sized problems. The proposed algorithm should thus be useful to both practitioners and researchers.

W.-T. Wu

Papers

A tabu search approach to the cell formation problem