Abstract
This paper proposes a genetic algorithm (GA) to solve an integrated mathematical model for dynamic cellular manufacturing system (DCMS) and production planning (PP) concurrently. The model simultaneously seeks to determine the variables associated with the production planning and the cell construction and formation. The total costs include the cost of machine procurement, the cell reconfiguration cost, the cell setup cost, the unexpected variable costs of cells alongside the production planning costs. At first the mathematical model, which is an integer nonlinear programming (INLP), is converted to a linear programming (LP) model. Then, the branch and bound (B&B) method is used for solving small size problems employing the Lingo 8 software. Finally because the problem is NP- hard, a GA is used to solve the large-scale problems as a meta-heuristic algorithm. To evaluate the results obtained by the genetic algorithm, they are compared with those obtained with the Lingo 8 software. Computational results confirm that the genetic algorithm is able to produce good solutions.
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References
Selim, H.M., Askin, R.G., Vakharia, A.J.: Cell formation in group technology: review, evaluation and directions for future research. Comput. Ind. Eng. 34(1), 3–20 (1998)
Askin, R.G., Estrada, S.: Investigation of Cellular Manufacturing Practices. Wiley, New York (1999)
Wemmerlöv, U., Hyer, N.L.: Cellular manufacturing in the US industry: a survey of users. Int. J. Prod. Res. 27(9), 1511–1530 (1989)
Reisman, A., Kumar, A., Motwani, J., Cheng, C.H.: Cellular manufacturing: a statistical review of the literature (1965–1995). Oper. Res. 45(4), 508–520 (1997)
Rheault, M., Drolet, J.R., Abdulnour, G.: Physically reconfigurable virtual cells: a dynamic model for a highly dynamic environment. Comput. Ind. Eng. 29(1–4), 221–225 (1995)
Balakrishnan, J., Cheng, C.H.: Multi-period planning and uncertainty issues in cellular manufacturing: a review and future directions. Eur. J. Oper. Res. 177(1), 281–309 (2007)
Defersha, F., Chen, M.: A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems. Int. J. Prod. Res. 46(22), 6389–6413 (2008)
Rezaeian, J., Javadian, N., Tavakkoli-Moghaddam, R., Jolai, F.: A hybrid approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system. Appl. Soft Comput. 11(6), 4195–4202 (2011)
Mehdizadeh, E., Rahimi, V.: An integrated mathematical model for solving dynamic cell formation problem considering operator assignment and inter/intra cell layouts. Appl. Soft Comput. 42, 325–341 (2016)
Chen, M., Cao, D.: Coordinating production planning in cellular manufacturing environment using Tabu search. Comput. Ind. Eng. 46(3), 571–588 (2004)
Bulgak, A.A., Bektas, T.: Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. Eur. J. Oper. Res. 192(2), 414–428 (2009)
Aghajani-Delavar, N., Mehdizadeh, E., Torabi, S., Tavakkoli-Moghaddam, R.: Design of a new mathematical model for integrated dynamic cellular manufacturing systems and production planning. Int. J. Eng.-Trans. B Appl. 28(5), 746 (2014)
Mehdizadeh, E., Niaki, S.V.D., Rahimi, V.: A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training. Comput. Ind. Eng. 101, 35–52 (2016)
Sakhaii, M., Tavakkoli-Moghaddam, R., Bagheri, M., Vatani, B.: A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Appl. Mathematical Modelling 40(1), 169–191 (2016)
Rafiei, H., Ghodsi, R.: A bi-objective mathematical model toward dynamic cell formation considering labor utilization. Appl. Math. Model. 37(4), 2308–2316 (2013)
Kia, R., Khaksar-Haghani, F., Javadian, N., Tavakkoli-Moghaddam, R.: Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm. J. Manuf. Syst. 33(1), 218–232 (2014)
Deep, K., Singh, P.K.: Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm. J. Manuf. Syst. 35, 155–163 (2015)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence, pp. 439–444. University of Michigan Press, Ann Arbor (1975)
Goldberg, D.E.: Genetic Alogorithms in Search (Optimization and Machine Learning). Addison-Wesley, Massachusetts (1989)
Gupta, Y.P., Gupta, M.C., Kumar, A., Sundram, C.: Minimizing total intercell and intracell moves in cellular manufacturing: a genetic algorithm approach. Int. J. Comput. Integr. Manuf. 8(2), 92–101 (1995)
Deljoo, V., Mirzapour Al-e-hashem, S., Deljoo, F., Aryanezhad, M.: Using genetic algorithm to solve dynamic cell formation problem. Appl. Math. Model. 34(4), 1078–1092 (2010)
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Mehdizadeh, E., Shamoradifar, M. (2019). A Genetic Algorithm for Solving a Dynamic Cellular Manufacturing System. In: Borangiu, T., Trentesaux, D., Thomas, A., Cavalieri, S. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing. SOHOMA 2018. Studies in Computational Intelligence, vol 803. Springer, Cham. https://doi.org/10.1007/978-3-030-03003-2_30
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DOI: https://doi.org/10.1007/978-3-030-03003-2_30
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