How to cite this paper
Jafari, H., Soltani, A & Soltani, M. (2013). Measuring the performance of FCM versus PSO for fuzzy clustering problems.International Journal of Industrial Engineering Computations , 4(3), 387-392.
Refrences
Al-Ahmari, A.M.A.(2002). A fuzzy analysis approach for part-machine grouping in cellular manufacturing systems. Integrated Manufacturing System, 13(7), 489-497.
Alizadeh, M., Gharakhani, M., Fotoohi, E., & Rada, R. (2011). Design and analysis of experiments in ANFIS modeling for stock price prediction. International Journal of Industrial Engineering Computations, 2(2), 409-418.
Al-Ahmari, A. M. A. (2002). A fuzzy analysis approach for part-machine grouping in cellular manufacturing systems. Integrated Manufacturing Systems,13(7), 489-497.
Andres, A., & Lozano, S. (2006). A particle swarm optimization algorithm for part–machine grouping. Robotics and Computer-Integrated Manufacturing, 22, 468-474.
Ballakur, A., & Harold, J. S. (1987). A within-cell utilization based heuristic for designing cellular manufacturing systems. International Journal of Production Research, 25(5), 639-665.
Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press: New York.
Chen, C.Y., & Ye, F. (2006). Adaptive hyper-fuzzy partition particle swarm optimization clustering algorithm. Cybernetics and Systems, 37, 463-479.
Chu, C.H., & Hayya, J.C. (1991). A fuzzy clustering approach to manufacturing cell formation. International Journal of Production Research, 29, 1475-1487.
Feizollahi, S., Shirmohammadi, A., & Kahreh, Z. S. (2012). Investigation the requirements of supply and distribution emergency logistics management and categorization its sub-criteria using AHP: a case study. Management Science Letters, 2, 2335–2340.
Gindy, N. N. Z., Ratchev, T. M., & Case, K. (1995). Component grouping for GT applications—a fuzzy clustering approach with validity measure. International Journal of Production Research, 33(9), 2493-2509.
Ghosh, T., Sengupta, S., Chattopadhyay, M., & Dan, P. K. (2010). Meta-heuristics in cellular manufacturing: A state-of-the-art review. International Journal of Industrial Engineering Computations, 2, 87-122.
Güng?r, Z., & Ar?kan, F. (2000). Application of fuzzy decision making in part-machine grouping. International Journal of Production Economics, 63(2), 181-193.
JabalAmeli, M. S., & Mortezaei, M. (2011). A hybrid model for multi-objective capacitated facility location network design problem. International Journal of Industrial Engineering Computations, 2, 509-524.
Josien, K., & Liao, T. W. (2000). Integrated use of fuzzy c-means and fuzzy KNN for GT part family and machine cell formation. International Journal of Production Research, 38(15), 3513-3536.
Li, J., Chu, C.H., Wang, Y., & Yan, W. (2007). An improved fuzzy clustering method for cellular manufacturing. International Journal of Production Research, 45, 1049-1062.
Molleman, E., Slomp, J., & Rolefes, S. (2002). The evolution of a cellular manufacturing system–a longitudinal case study. International Journal of Production Economics, 75(3), 305-322.
Selim, H. M., Askin, R. G., & Vakharia, A. J. (1998). Cell formation in group technology: review, evaluation and directions for future research. Computers & Industrial Engineering, 34(1), 3-20.
Singh, N. (1993). Design of cellular manufacturing systems: an invited review. European Journal of Operational Research, 69(3), 284-291.
Venugopal, V. (1999). Soft-computing-based approaches to the group technology problem: a state-of-the-art review. International journal of production research, 37(14), 3335-3357.
Xu, H., & Wang, H.P. (1989). Part family formation for GT applications based on fuzzy mathematics. International Journal of Production Research, 27, 1637-1651.
Yang, M.S., Hung, W.L., & Cheng, F.C. (2006). Mixed-variable fuzzy clustering approach to part family and machine cell formation for GT applications. International Journal of Production Economics, 103, 185-198.
Yin, Y., & Yasuda, K. (2006). Similarity coefficient methods applied to the cell formation problem: A taxonomy and review. International Journal of Production Economics, 101(2), 329-352.
Alizadeh, M., Gharakhani, M., Fotoohi, E., & Rada, R. (2011). Design and analysis of experiments in ANFIS modeling for stock price prediction. International Journal of Industrial Engineering Computations, 2(2), 409-418.
Al-Ahmari, A. M. A. (2002). A fuzzy analysis approach for part-machine grouping in cellular manufacturing systems. Integrated Manufacturing Systems,13(7), 489-497.
Andres, A., & Lozano, S. (2006). A particle swarm optimization algorithm for part–machine grouping. Robotics and Computer-Integrated Manufacturing, 22, 468-474.
Ballakur, A., & Harold, J. S. (1987). A within-cell utilization based heuristic for designing cellular manufacturing systems. International Journal of Production Research, 25(5), 639-665.
Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press: New York.
Chen, C.Y., & Ye, F. (2006). Adaptive hyper-fuzzy partition particle swarm optimization clustering algorithm. Cybernetics and Systems, 37, 463-479.
Chu, C.H., & Hayya, J.C. (1991). A fuzzy clustering approach to manufacturing cell formation. International Journal of Production Research, 29, 1475-1487.
Feizollahi, S., Shirmohammadi, A., & Kahreh, Z. S. (2012). Investigation the requirements of supply and distribution emergency logistics management and categorization its sub-criteria using AHP: a case study. Management Science Letters, 2, 2335–2340.
Gindy, N. N. Z., Ratchev, T. M., & Case, K. (1995). Component grouping for GT applications—a fuzzy clustering approach with validity measure. International Journal of Production Research, 33(9), 2493-2509.
Ghosh, T., Sengupta, S., Chattopadhyay, M., & Dan, P. K. (2010). Meta-heuristics in cellular manufacturing: A state-of-the-art review. International Journal of Industrial Engineering Computations, 2, 87-122.
Güng?r, Z., & Ar?kan, F. (2000). Application of fuzzy decision making in part-machine grouping. International Journal of Production Economics, 63(2), 181-193.
JabalAmeli, M. S., & Mortezaei, M. (2011). A hybrid model for multi-objective capacitated facility location network design problem. International Journal of Industrial Engineering Computations, 2, 509-524.
Josien, K., & Liao, T. W. (2000). Integrated use of fuzzy c-means and fuzzy KNN for GT part family and machine cell formation. International Journal of Production Research, 38(15), 3513-3536.
Li, J., Chu, C.H., Wang, Y., & Yan, W. (2007). An improved fuzzy clustering method for cellular manufacturing. International Journal of Production Research, 45, 1049-1062.
Molleman, E., Slomp, J., & Rolefes, S. (2002). The evolution of a cellular manufacturing system–a longitudinal case study. International Journal of Production Economics, 75(3), 305-322.
Selim, H. M., Askin, R. G., & Vakharia, A. J. (1998). Cell formation in group technology: review, evaluation and directions for future research. Computers & Industrial Engineering, 34(1), 3-20.
Singh, N. (1993). Design of cellular manufacturing systems: an invited review. European Journal of Operational Research, 69(3), 284-291.
Venugopal, V. (1999). Soft-computing-based approaches to the group technology problem: a state-of-the-art review. International journal of production research, 37(14), 3335-3357.
Xu, H., & Wang, H.P. (1989). Part family formation for GT applications based on fuzzy mathematics. International Journal of Production Research, 27, 1637-1651.
Yang, M.S., Hung, W.L., & Cheng, F.C. (2006). Mixed-variable fuzzy clustering approach to part family and machine cell formation for GT applications. International Journal of Production Economics, 103, 185-198.
Yin, Y., & Yasuda, K. (2006). Similarity coefficient methods applied to the cell formation problem: A taxonomy and review. International Journal of Production Economics, 101(2), 329-352.