How to cite this paper
Delgoshaei, A., Delgoshaei, A & Ali, A. (2019). Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review.International Journal of Industrial Engineering Computations , 10(2), 177-198.
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Adenso-Diaz, B., Lozano, S., & Eguía, I. (2005). Part-machine grouping using weighted similarity coefficients. Computers & industrial engineering, 48(3), 553-570.
Agarwal, A. (2008). Partitioning bottleneck work center for cellular manufacturing: An integrated performance and cost model. International Journal of Production Economics, 111(2), 635-647.
Al-sultan, K. S. (1997). A hard clustering approach to the part family formation problem. Production Planning & Control, 8(3), 231-236.
Angra, S., Sehgal, R., & Samsudeen Noori, Z. (2008). Cellular manufacturing—A time-based analysis to the layout problem. International Journal of Production Economics, 112(1), 427-438.
Ariafar, S., Firoozi, Z., & Ismail, N. (2014). A Triangular Stochastic Facility Layout Problem in a Cellular Manufacturing System. Paper presented at the International Conference on Mathematical Sciences and Statistics 2013.
Arkat, J., Hosseini, L., & Farahani, M. H. (2011). Minimization of exceptional elements and voids in the cell formation problem using a multi-objective genetic algorithm. Expert Systems with Applications, 38(8), 9597-9602.
Ashayeri, J., Heuts, R., & Tammel, B. (2005). A modified simple heuristic for the p-median problem, with facilities design applications. Robotics and Computer-Integrated Manufacturing, 21(4), 451-464.
Baker, R., & Maropoulos, P. G. (2000). Cell design and continuous improvement. International Journal of Computer Integrated Manufacturing, 13(6), 522-532.
Balakrishnan, J., & Cheng, C. H. (2005). Dynamic cellular manufacturing under multiperiod planning horizons. Journal of Manufacturing Technology Management, 16(5), 516-530.
Banerjee, I., & Das, P. (2012). Group technology based adaptive cell formation using predator–prey genetic algorithm. Applied Soft Computing, 12(1), 559-572.
Basheer, I., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31.
Berardi, V. L., Zhang, G., & Felix Offodile, O. (1999). A mathematical programming approach to evaluating alternative machine clusters in cellular manufacturing. International Journal of Production Economics, 58(3), 253-264.
Boe, W. J., & Cheng, C. H. (1991). A close neighbour algorithm for designing cellular manufacturing systems. International Journal of Production Research, 29(10), 2097-2116.
Burke, L., & Kamal, S. (1995). Neural networks and the part family/machine group formation problem in cellular manufacturing: a framework using fuzzy ART. Journal of Manufacturing Systems, 14(3), 148-159.
Chan, F. T., Lau, K. W., Chan, P. L. Y., & Choy, K. L. (2006). Two-stage approach for machine-part grouping and cell layout problems. Robotics and Computer-Integrated Manufacturing, 22(3), 217-238.
Chandrasekharan, M., & Rajagopalan, R. (1987). ZODIAC—an algorithm for concurrent formation of part-families and machine-cells. International Journal of Production Research, 25(6), 835-850.
Chattopadhyay, M., Dan, P. K., & Mazumdar, S. (2012). Application of visual clustering properties of self organizing map in machine–part cell formation. Applied Soft Computing, 12(2), 600-610.
Chen, J.-S., & Heragu, S. S. (1999). Stepwise decomposition approaches for large scale cell formation problems. European Journal of Operational Research, 113(1), 64-79.
Chen, J.-S., & Park, S. (1996). An improved ART neural net for machine cell formation. Journal of Materials Processing Technology, 61(1), 1-6.
Chitta, R., & Narasimha Murty, M. (2010). Two-level k-means clustering algorithm for k–τ relationship establishment and linear-time classification. Pattern Recognition, 43(3), 796-804.
Chow, W. S., & Hawaleshka, O. (1992). An efficient algorithm for solving the machine chaining problem in cellular manufacturing. Computers & Industrial Engineering, 22(1), 95-100.
Defersha, F. M., & Chen, M. (2006). A comprehensive mathematical model for the design of cellular manufacturing systems. International Journal of Production Economics, 103(2), 767-783.
Delgoshaei, A., Ali, A., Ariffin, M. K. A., & Gomes, C. (2016a). A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty. Computers & Industrial Engineering, 100, 110-132.
Delgoshaei, A., Ariffin, M. K. A. M., Leman, Z., Baharudin, B. H. T. B., & Gomes, C. (2016b). Review of evolution of cellular manufacturing system’s approaches: Material transferring models. International Journal of Precision Engineering and Manufacturing, 17(1), 131-149.
Delgoshaei, A., & Gomes, C. (2016). A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost. Applied Soft Computing, 49, 27-55.
Deutsch, S. J., Freeman, S. F., & Helander, M. (1998). Manufacturing cell formation using an improved P-median model. Computers & Industrial Engineering, 34(1), 135-146.
Dimopoulos, C., & Mort, N. (2001). A hierarchical clustering methodology based on genetic programming for the solution of simple cell-formation problems. International Journal of Production Research, 39(1), 1-19.
Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104.
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Paper presented at the Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on.
Egilmez, G., & Süer, G. (2014). The impact of risk on the integrated cellular design and control. International Journal of Production Research, 52(5), 1455-1478.
Egilmez, G., Süer, G. A., & Huang, J. (2012). Stochastic cellular manufacturing system design subject to maximum acceptable risk level. Computers & Industrial Engineering, 63(4), 842-854.
Enke, D., Ratanapan, K., & Dagli, C. (1998). Machine-part family formation utilizing an ART1 neural network implemented on a parallel neuro-computer. Computers & Industrial Engineering, 34(1), 189-205.
Enke, D., Ratanapan, K., & Dagli, C. (2000). Large machine-part family formation utilizing a parallel ART1 neural network. Journal of Intelligent Manufacturing, 11(6), 591-604.
Goldengorin, B., Krushinsky, D., & Slomp, J. (2012). Flexible PMP approach for large-size cell formation. Operations Research, 60(5), 1157-1166.
Gu, P. (1991). Process-based machine grouping for cellular manufacturing systems. Computers in Industry, 17(1), 9-17.
Guerrero, F., Lozano, S., Smith, K. A., Canca, D., & Kwok, T. (2002). Manufacturing cell formation using a new self-organizing neural network. Computers & Industrial Engineering, 42(2), 377-382.
Gupta, T. (1991). Clustering algorithms for the design of a cellular manufacturing system—an analysis of their performance. Computers & Industrial Engineering, 20(4), 461-468.
Gupta, T., & Seifoddini, H. I. (1990). Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system. The International Journal of Production Research, 28(7), 1247-1269.
Irani, S. A., & Huang, H. (2000). Custom design of facility layouts for multiproduct facilities using layout modules. Robotics and Automation, IEEE Transactions on, 16(3), 259-267.
Izakian, H., & Abraham, A. (2011). Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Systems with Applications, 38(3), 1835-1838.
Jaśkowska, J., Drabczyk, A., Kułaga, D., Zaręba, P., & Majka, Z. (2018). Solvent-free microwave-assisted synthesis of aripiprazole. Current Chemistry Letters, 7(3), 81-86.
Jeon, G., & Leep, H. R. (2006). Forming part families by using genetic algorithm and designing machine cells under demand changes. Computers & Operations Research, 33(1), 263-283.
Josien, K., & Liao, T. W. (2002). Simultaneous grouping of parts and machines with an integrated fuzzy clustering method. Fuzzy Sets and Systems, 126(1), 1-21.
Kao, Y., & Li, Y. (2008). Ant colony recognition systems for part clustering problems. International Journal of Production Research, 46(15), 4237-4258.
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Kulkarni, U. R., & Kiang, M. Y. (1995). Dynamic grouping of parts in flexible manufacturing systems—a self-organizing neural networks approach. European Journal of Operational Research, 84(1), 192-212.
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Lee-Post, A. (2000). Part family identification using a simple genetic algorithm. International Journal of Production Research, 38(4), 793-810.
Lozano, S., Dobado, D., Larrañeta, J., & Onieva, L. (2002). Modified fuzzy C-means algorithm for cellular manufacturing. Fuzzy sets and systems, 126(1), 23-32.
Mahdavi, I., Aalaei, A., Paydar, M. M., & Solimanpur, M. (2012). A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system. Journal of Manufacturing Systems, 31(2), 214-223.
Mahdavi, I., Javadi, B., Fallah-Alipour, K., & Slomp, J. (2007). Designing a new mathematical model for cellular manufacturing system based on cell utilization. Applied Mathematics and Computation, 190(1), 662-670.
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Al-sultan, K. S. (1997). A hard clustering approach to the part family formation problem. Production Planning & Control, 8(3), 231-236.
Angra, S., Sehgal, R., & Samsudeen Noori, Z. (2008). Cellular manufacturing—A time-based analysis to the layout problem. International Journal of Production Economics, 112(1), 427-438.
Ariafar, S., Firoozi, Z., & Ismail, N. (2014). A Triangular Stochastic Facility Layout Problem in a Cellular Manufacturing System. Paper presented at the International Conference on Mathematical Sciences and Statistics 2013.
Arkat, J., Hosseini, L., & Farahani, M. H. (2011). Minimization of exceptional elements and voids in the cell formation problem using a multi-objective genetic algorithm. Expert Systems with Applications, 38(8), 9597-9602.
Ashayeri, J., Heuts, R., & Tammel, B. (2005). A modified simple heuristic for the p-median problem, with facilities design applications. Robotics and Computer-Integrated Manufacturing, 21(4), 451-464.
Baker, R., & Maropoulos, P. G. (2000). Cell design and continuous improvement. International Journal of Computer Integrated Manufacturing, 13(6), 522-532.
Balakrishnan, J., & Cheng, C. H. (2005). Dynamic cellular manufacturing under multiperiod planning horizons. Journal of Manufacturing Technology Management, 16(5), 516-530.
Banerjee, I., & Das, P. (2012). Group technology based adaptive cell formation using predator–prey genetic algorithm. Applied Soft Computing, 12(1), 559-572.
Basheer, I., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31.
Berardi, V. L., Zhang, G., & Felix Offodile, O. (1999). A mathematical programming approach to evaluating alternative machine clusters in cellular manufacturing. International Journal of Production Economics, 58(3), 253-264.
Boe, W. J., & Cheng, C. H. (1991). A close neighbour algorithm for designing cellular manufacturing systems. International Journal of Production Research, 29(10), 2097-2116.
Burke, L., & Kamal, S. (1995). Neural networks and the part family/machine group formation problem in cellular manufacturing: a framework using fuzzy ART. Journal of Manufacturing Systems, 14(3), 148-159.
Chan, F. T., Lau, K. W., Chan, P. L. Y., & Choy, K. L. (2006). Two-stage approach for machine-part grouping and cell layout problems. Robotics and Computer-Integrated Manufacturing, 22(3), 217-238.
Chandrasekharan, M., & Rajagopalan, R. (1987). ZODIAC—an algorithm for concurrent formation of part-families and machine-cells. International Journal of Production Research, 25(6), 835-850.
Chattopadhyay, M., Dan, P. K., & Mazumdar, S. (2012). Application of visual clustering properties of self organizing map in machine–part cell formation. Applied Soft Computing, 12(2), 600-610.
Chen, J.-S., & Heragu, S. S. (1999). Stepwise decomposition approaches for large scale cell formation problems. European Journal of Operational Research, 113(1), 64-79.
Chen, J.-S., & Park, S. (1996). An improved ART neural net for machine cell formation. Journal of Materials Processing Technology, 61(1), 1-6.
Chitta, R., & Narasimha Murty, M. (2010). Two-level k-means clustering algorithm for k–τ relationship establishment and linear-time classification. Pattern Recognition, 43(3), 796-804.
Chow, W. S., & Hawaleshka, O. (1992). An efficient algorithm for solving the machine chaining problem in cellular manufacturing. Computers & Industrial Engineering, 22(1), 95-100.
Defersha, F. M., & Chen, M. (2006). A comprehensive mathematical model for the design of cellular manufacturing systems. International Journal of Production Economics, 103(2), 767-783.
Delgoshaei, A., Ali, A., Ariffin, M. K. A., & Gomes, C. (2016a). A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty. Computers & Industrial Engineering, 100, 110-132.
Delgoshaei, A., Ariffin, M. K. A. M., Leman, Z., Baharudin, B. H. T. B., & Gomes, C. (2016b). Review of evolution of cellular manufacturing system’s approaches: Material transferring models. International Journal of Precision Engineering and Manufacturing, 17(1), 131-149.
Delgoshaei, A., & Gomes, C. (2016). A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost. Applied Soft Computing, 49, 27-55.
Deutsch, S. J., Freeman, S. F., & Helander, M. (1998). Manufacturing cell formation using an improved P-median model. Computers & Industrial Engineering, 34(1), 135-146.
Dimopoulos, C., & Mort, N. (2001). A hierarchical clustering methodology based on genetic programming for the solution of simple cell-formation problems. International Journal of Production Research, 39(1), 1-19.
Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104.
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Paper presented at the Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on.
Egilmez, G., & Süer, G. (2014). The impact of risk on the integrated cellular design and control. International Journal of Production Research, 52(5), 1455-1478.
Egilmez, G., Süer, G. A., & Huang, J. (2012). Stochastic cellular manufacturing system design subject to maximum acceptable risk level. Computers & Industrial Engineering, 63(4), 842-854.
Enke, D., Ratanapan, K., & Dagli, C. (1998). Machine-part family formation utilizing an ART1 neural network implemented on a parallel neuro-computer. Computers & Industrial Engineering, 34(1), 189-205.
Enke, D., Ratanapan, K., & Dagli, C. (2000). Large machine-part family formation utilizing a parallel ART1 neural network. Journal of Intelligent Manufacturing, 11(6), 591-604.
Goldengorin, B., Krushinsky, D., & Slomp, J. (2012). Flexible PMP approach for large-size cell formation. Operations Research, 60(5), 1157-1166.
Gu, P. (1991). Process-based machine grouping for cellular manufacturing systems. Computers in Industry, 17(1), 9-17.
Guerrero, F., Lozano, S., Smith, K. A., Canca, D., & Kwok, T. (2002). Manufacturing cell formation using a new self-organizing neural network. Computers & Industrial Engineering, 42(2), 377-382.
Gupta, T. (1991). Clustering algorithms for the design of a cellular manufacturing system—an analysis of their performance. Computers & Industrial Engineering, 20(4), 461-468.
Gupta, T., & Seifoddini, H. I. (1990). Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system. The International Journal of Production Research, 28(7), 1247-1269.
Irani, S. A., & Huang, H. (2000). Custom design of facility layouts for multiproduct facilities using layout modules. Robotics and Automation, IEEE Transactions on, 16(3), 259-267.
Izakian, H., & Abraham, A. (2011). Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Systems with Applications, 38(3), 1835-1838.
Jaśkowska, J., Drabczyk, A., Kułaga, D., Zaręba, P., & Majka, Z. (2018). Solvent-free microwave-assisted synthesis of aripiprazole. Current Chemistry Letters, 7(3), 81-86.
Jeon, G., & Leep, H. R. (2006). Forming part families by using genetic algorithm and designing machine cells under demand changes. Computers & Operations Research, 33(1), 263-283.
Josien, K., & Liao, T. W. (2002). Simultaneous grouping of parts and machines with an integrated fuzzy clustering method. Fuzzy Sets and Systems, 126(1), 1-21.
Kao, Y., & Li, Y. (2008). Ant colony recognition systems for part clustering problems. International Journal of Production Research, 46(15), 4237-4258.
Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis (Vol. 344): Wiley. com.
King, J. R. (1980). Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm. International Journal of Production Research, 18(2), 213-232.
King, J. R., & Nakornchai, V. (1982). Machine-component group formation in group technology: review and extension. International Journal of Production Research, 20(2), 117-133.
Kohonen, T. (1989). Self-organizing and associative memory. Springer, Berlin.
Krushinsky, D., & Goldengorin, B. (2012). An exact model for cell formation in group technology. Computational Management Science, 9(3), 323-338.
Kulkarni, U. R., & Kiang, M. Y. (1995). Dynamic grouping of parts in flexible manufacturing systems—a self-organizing neural networks approach. European Journal of Operational Research, 84(1), 192-212.
Kuo, R., Su, Y., Chiu, C., Chen, K.-Y., & Tien, F.-C. (2006). Part family formation through fuzzy ART2 neural network. Decision Support Systems, 42(1), 89-103.
Kusiak, A. (1987). The generalized group technology concept. International Journal of Production Research, 25(4), 561-569.
Kusiak, A. (1991). Branching algorithms for solving the group technology problem. Journal of Manufacturing Systems, 10(4), 332-343.
Lee-Post, A. (2000). Part family identification using a simple genetic algorithm. International Journal of Production Research, 38(4), 793-810.
Lozano, S., Dobado, D., Larrañeta, J., & Onieva, L. (2002). Modified fuzzy C-means algorithm for cellular manufacturing. Fuzzy sets and systems, 126(1), 23-32.
Mahdavi, I., Aalaei, A., Paydar, M. M., & Solimanpur, M. (2012). A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system. Journal of Manufacturing Systems, 31(2), 214-223.
Mahdavi, I., Javadi, B., Fallah-Alipour, K., & Slomp, J. (2007). Designing a new mathematical model for cellular manufacturing system based on cell utilization. Applied Mathematics and Computation, 190(1), 662-670.
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