A fuzzy simulated evolution algorithm for integrated manufacturing system design

Abstract: Integrated cell formation and layout (CFLP) is an extended application of the group technology philosophy in which machine cells and cell layout are addressed simultaneously. The aim of this technological innovation is to improve both productivity and flexibility in modern manufacturing industry. However, due to its combinatorial complexity, the cell formation and layout problem is best solved by heuristic and metaheuristic approaches. As CFLP is prevalent in manufacturing industry, developing robust and efficient solution methods for the problem is imperative. This study seeks to develop a fuzzy simulated evolution algorithm (FSEA) that integrates fuzzy-set theoretic concepts and the philosophy of constructive perturbation and evolution. Deriving from the classical simulated evolution algorithm, the search efficiency of the major phases of the algorithm is enhanced, including initialization, evaluation, selection and reconstruction. Illustrative computational experiments based on existing problem instances from the literature demonstrate the utility and the strength of the FSEA algorithm developed in this study. It is anticipated in this study that the application of the algorithm can be extended to other complex combinatorial problems in industry.

How to cite this paper

 Mutingi, M. (2013). A fuzzy simulated evolution algorithm for integrated manufacturing system design.International Journal of Industrial Engineering Computations , 4(2), 177-190.

Refrences

Aryanezhad, M. B., Aliabadi, J., & Tavakkoli-Moghaddam, R. (2011). A new approach for cell formation and scheduling with assembly operations and product structure. International Journal of Industrial Engineering Computations, 2(3), 533-546.

Dubois, D., & Prade, H. (1980). Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York.

Filho, E.V.G., & Tiberti, A.J. (2006).A group genetic algorithm for the machine cell formation problem’, International Journal of Production Economics, 102, 1-21.

Forghani, K., Khamseh, A. A., & Mohammadi, M. (2012).Integrated quadratic assignment and continuous facility layout problem.International Journal of Industrial Engineering Computations, 3(5), 787-806.

Ghezavati, V. R. (2011). A new stochastic mixed integer programming to design integrated cellular manufacturing system: A supply chain framework. International Journal of Industrial Engineering Computations, 2(3), 563-574.

Goldberg, D.E. (1989). Genetic Algorithm in Search, Optimization, and Machine Learning’, Addison-Wesley, Reading, MA.

Ghosh, T., Sengupta, S., Chattopadhyay, M., & Dan, P. K. (2011). Meta-heuristics in cellular manufacturing: A state-of-the-art review. International Journal of Industrial Engineering Computations, 2(1), 87-122.

Hamedi, M., Ismail, N., Esmaeilian, G. R., & Ariffin, M. K. A. (2012).Virtual cellular manufacturing system based on resource element approach and analyzing its performance over different basic layouts.International Journal of Industrial EngineeringComputations, 3(2), 265-276.

Harhalakis, G. Nagi, R., & Proth J.M. (1990).An efficient heuristic in manufacturing cell formation for group technology applications.International Journal of Production Research, 28, 185-198.

Jayaswal, S. and Adil, G. K. ( 2004). Efficient algorithm for cell formation with sequence data, machine replications and alternative process routings.International Journal of Production Research, 42, 2419-2433.

Kling, R.M., & Banejee, P. (1987). ESP: A New Standard Cell Placement Package Using Simulated Evolution. Proceedings of the 24th ACWIEEE Design Automation Conference, 60-66.

Li, J., & Kwan, R.S.K. (2002). A fuzzy evolutionary approach with Taguchi parameter setting for the set covering problem. Proceedings of the 2002 IEEE Congress on Evolutionary Computation, 1203-1208.

Lin, Y.L., Hsu, Y.-C., & Tsai, F.S. (1989). SILK: A Simulated Evolution Router. IEEE Transaction on Computer-Aided Design, 8, 1108-1114.

Ly, T.A. & Mowchenko, J.T. (1993). Applying Simulated Evolution to High Level Synthesis. IEEE Transaction on Computer-Aided Design of Integrated Circuits and Systems, 12, 389-409.

Mahdavi, I. & Mahadevan, B. (2008). CLASS: An algorithm for cellular manufacturing system and layout design using sequence data. Robotics and Computer-Integrated Manufacturing, 24, 488-497.

Mahdavi, M. I., Tahami, B. N., Teymourian, E. (2010). A new cell formation problem with the consideration of multifunctional machines and in-route machines dissimilarity - A two phase solution approach.IEEE 17th International Conference on Industrial Engineering and Engineering Management, 2010, 475-479.

Mutingi, M. & Mbohwa, C., Mhlanga, S., & Goriwondo, W. (2012). Integrated cell manufacturing system design. Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, 2012, 254-264.

Mutingi, M., & Onwubolu, G.C. (2012). Integrated cellular manufacturing system design and layout using group genetic algorithms. Book Chapter, Manufacturing Systems, Intech Publishers, 205-222.

Nair, G. J., & Narendran, T. T. (1998). CASE: a clustering algorithm for cell formation with sequence data. International Journal of Production Research, 36, 157-179.

Rao, R. V., & Singh, D. (2012). Weighted euclidean distance based approach as a multiple attribute decision making method for plant or facility layout design selection. International Journal of Industrial Engineering Computations, 3(3), 365-382.

Saiti, S.M., Youssef, H. & Ali, H. (1999).Fuzzy Simulated Evolution Algorithm for Multi-objective Optimization of VLSI Placement.Congress on Evolutionary Computation, 91-97.

Saiti, SM., & AI-Ismail, M.S. (2004). Enhanced simulated evolution algorithm For digital Circuit Design Yielding Faster Execution in a Larger Solution Space, IEEE Congress on Evolutionary Computation, 2004. CEC2004, 2, 1794-1799.

Singh, N. (1993). Design of cellular manufacturing systems: an invited review. European Journal of Operational Research, 69(3), 284--291.

Tam, K.Y., (1988). An operation sequence based similarity coefficient for part family formations. Journal of Manufacturing Systems, 9, 55-68.

Veldhuizen, D. A. V., & Lamont, G. B. (2000). Multi-objective evolutionary algorithms: Analyzing the state-of-the- Art. Evolutionary Computation, 8(2), pp.125-147.

Won, Y., & Lee, K. C. (2001).Group technology cell formation considering operation sequences and production volumes.International Journal of Production Research, 39, 2755-2768.

Zadeh, L.A. (1965). Fuzzy Sets.Information and Control, 8, 338-353.