Applying simulated annealing to cellular manufacturing system design

Cell formation and cellular layout design are the two main steps in designing a cellular manufacturing system (CMS). In this paper, we will present an integrated methodology based on a new concept of similarity coefficients and the use of simulated annealing (SA) as an optimization tool. In comparison with the previous works, the proposed methodology takes into account relevant production data, such as alternative process routings and the production volumes of parts. The SA-based optimization tool is parallel in nature and, hence, can reduce the computation time significantly, so it is capable of handling large-scale problems. Finally, the SA-based procedure is compared with a genetic algorithm (GA) based procedure and it will be shown that the SA-based algorithm can be as effective as a GA-based algorithm, but with less computational time and effort.

[1]  N. Singh,et al.  Design of cellular manufacturing systems: An invited review , 1993 .

[2]  Divakar Rajamani,et al.  A mathematical model for cell formation considering investment and operational costs , 1993 .

[3]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[4]  M Kazerooni,et al.  Genetic Algorithms in Manufacturing System Design , 2000 .

[5]  R. Lashkari,et al.  A mathematical programming approach to joint cell formation and operation allocation in cellular manufacturing , 1995 .

[6]  Andrew Kusiak,et al.  The part families problem in flexible manufacturing systems , 1985 .

[7]  Hamid R. Parsaei,et al.  Part family formation based on a new similarity coefficient which considers alternative routes during machine failure , 1998 .

[8]  C.-T. Su,et al.  Optimization of machining conditions for turning cylindrical stocks into continuous finished profiles , 1998 .

[9]  C.-L. Chen,et al.  A simulated annealing solution to the cell formation problem , 1995 .

[10]  Mary E. Helander,et al.  Manufacturing cell formation using an improved p -median model , 1998 .

[11]  K. Y. Tam,et al.  An operation sequence based similarity coefficient for part families formations , 1990 .

[12]  Chih-Ming Hsu,et al.  Multi-objective machine-part cell formation through parallel simulated annealing , 1998 .

[13]  Pedro M. Vilarinho,et al.  A simulated annealing approach for manufacturing cell formation with multiple identical machines , 2003, Eur. J. Oper. Res..

[14]  G. K. Adil,et al.  Assignment allocation and simulated annealing algorithms for cell formation , 1997 .

[15]  Lee Luong A cellular similarity coefficient algorithm for the design of manufacturing cells , 1993 .

[16]  G. Srinivasan A clustering algorithm for machine cell formation in group technology using minimum spanning trees , 1994 .

[17]  Jun Wang Computational Intelligence In Manufacturing Handbook , 2000 .

[18]  F. Boctor A Jinear formulation of the machine-part cell formation problem , 1991 .

[19]  Hamid Seifoddini,et al.  Comparative study of similarity coefficients and clustering algorithms in cellular manufacturing , 1994 .

[20]  S. Shekhar,et al.  Evaluation of search algorithms and clustering efficiency measures for machine-part matrix clustering , 1995 .

[21]  T. T. Narendran,et al.  Machine-cell formation through neural network models , 1994 .