ارائه یک مدل ریاضی با رویکرد بهینه سازی استوار برای طراحی سیستم تولید سلولی پویا با در نظرگیری ماشین آلات چندکاره

[1]     Mitrofanov, S., (1966). “Scientific Principles of Group Technology, Part I”, National Lending Library of Science and Technology, Boston, MA

[2]     Burbidge, J.L., (1975). The introduction of group technology: Heinemann London.

[3]     Deep, K., Singh, P.K., (2015). “Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm”, Journal of Manufacturing Systems, 35: 155-163.

[4]     Renna, P.,  Ambrico, M., (2015) “Design and reconfiguration models for dynamic cellular manufacturing to handle market changes”, International Journal of Computer Integrated Manufacturing,  28: 170-186.

[5]     Esmailnezhad, B., Fattahi, P., Kheirkhah, A.S., (2015). “A stochastic model for the cell formation problem considering machine reliability," Journal of Industrial Engineering International,  1-15.

[6]     Yadollahi, M.S., Mahdavi, I., Paydar, M.M., Jouzdani, J., (2014). “Design a bi-objective mathematical model for cellular manufacturing systems considering variable failure rate of machines”, International Journal of Production Research, 52: 7401-7415.

[7]     Javadi, B., Jolai, F., Slomp, J., Rabbani, M., Tavakkoli-Moghaddam, R., (2013). “An integrated approach for the cell formation and layout design in cellular manufacturing systems”, International Journal of Production Research, 51: 6017-6044.

[8]     R. Kia, Shirazi, H., Javadian, N., Tavakkoli-Moghaddam, R., (2013). “A multi-objective model for designing a group layout of a dynamic cellular manufacturing system”, Journal of Industrial Engineering International, 9: 1-14.

[9]     Saidi-Mehrabad, M., Paydar, M.M., Aalaei, A., “Production planning and worker training in dynamic manufacturing systems”, Journal of Manufacturing Systems, 32: 308-314.

[10] Rafiei, H., Ghodsi, R., (2013). “A bi-objective mathematical model toward dynamic cell formation considering labor utilization”, Applied Mathematical Modelling, 37: 2308-2316.

[11] Bagheri, M., Bashiri, M., (2014). “A new mathematical model towards the integration of cell formation with operator assignment and inter-cell layout problems in a dynamic environment”, Applied Mathematical Modelling, vol. 38: 1237-1254.

[12] M.M. Paydar, Saidi-Mehrabad, M., Kia, R., (2013). “Designing a new integrated model for dynamic cellular manufacturing systems with production planning and intra-cell layout”, International Journal of Applied Decision Sciences,  6: 117-143.

[13] Dalfard, V.M., “New mathematical model for problem of dynamic cell formation based on number and average length of intra and intercellular movements”, Applied Mathematical Modelling, 37: 1884-1896.

[14]    پایدار، م.م. سعیدی مهرآباد، م.، (1393). "طراحی یک مدل یکپارچه استوار دو‌هدفه زنجیره تأمین و آرایش سلولی مجازی پویا"، نشریه پژوهش های مهندسی صنایع در سیستم های تولید،2: 33-35.

[15] Zeidi, J.R., Javadian, N., Tavakkoli-Moghaddam, R., Jolai, F., “A hybrid multi-objective approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system”, Computers & Industrial Engineering, 66: 1004-1014.

[16] Arkat, J., Farahani, M.H., Ahmadizar, F., “Multi-objective genetic algorithm for cell formation problem considering cellular layout and operations scheduling”, International Journal of Computer Integrated Manufacturing, 25: 625-635.

[17] Tavakkoli-Moghaddam, R., Ranjbar-Bourani, M., Amin, G.R., Siadat, A., (2012). “A cell formation problem considering machine utilization and alternative process routes by scatter search”, Journal of Intelligent Manufacturing, 23: 1127-1139.

[18] Kia, R., Baboli, A., Javadian, N., Tavakkoli-Moghaddam, R., Kazemi, M., Khorrami, J., (2012). “Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing”, Computers & Operations Research, 39: 2642-2658.

[19] Jolai, F., Tavakkoli-Moghaddam, R., Golmohammadi, A., Javadi, B., “An electromagnetism-like algorithm for cell formation and layout problem”, Expert Systems with Applications, 39: 2172-2182.

[20] Arkat, J., Farahani, M.H., Hosseini, L., “Integrating cell formation with cellular layout and operations scheduling”, The International Journal of Advanced Manufacturing Technology, 61: 637-647.

[21] Banerjee, I., Das, P., (2012). “Group technology based adaptive cell formation using predator–prey genetic algorithm”, Applied Soft Computing, 12: 559-572.

[22] Mahdavi, I., Aalaei, A., M.M. Paydar, Solimanpur, M., (2012). “A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system”, Journal of Manufacturing Systems, 31: 214-223.

[23] Javadian, N., Aghajani, A., Rezaeian, J., Sebdani, M.J.G., “A multi-objective integrated cellular manufacturing systems design with dynamic system reconfiguration”, The International Journal of Advanced Manufacturing Technology, 56: 307-317.

[24] Rafiee, K., Rabbani, M., Rafiei, H., Rahimi-Vahed, A.,  “A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system”, Applied Mathematical Modelling, 35: 1810-1819.

[25] Rezazadeh, H., Mahini, R., Zarei, M., (2011). “Solving a dynamic virtual cell formation problem by linear programming embedded particle swarm optimization algorithm”, Applied Soft Computing, 11: 3160-3169.

[26] Mahdavi, I., Aalaei, A., Paydar, M.M., Solimanpur, M., (2011) “Multi-objective cell formation and production planning in dynamic virtual cellular manufacturing systems”, International Journal of Production Research, 49: 6517-6537.

[27] Ghotboddini, M., Rabbani, M., Rahimian, H., (2011) “A comprehensive dynamic cell formation design: Benders’ decomposition approach”, Expert Systems with Applications,  38: 2478-2488.

[28] 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: 9597-9602.

[29] Chung, S.H., Wu, T.H., Chang, C.C., (2011). “An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations”, Computers & Industrial Engineering, 60: 7-15.

[30] Solimanpur, M., Foroughi, A., “A new approach to the cell formation problem with alternative processing routes and operation sequence”, International Journal of Production Research, 49: 5833-5849.

[31] Deljoo, V., Mirzapour Al-e-hashem, S., Deljoo, F., Aryanezhad, M., (2010). “Using genetic algorithm to solve dynamic cell formation problem”, Applied Mathematical Modelling, 34: 1078-1092.

[32] Mahdavi, I., Aalaei, A., Paydar, M.M., Solimanpur, M.,  "Designing a mathematical model for dynamic cellular manufacturing systems considering production planning and worker assignment," Computers & Mathematics with Applications, vol. 60, pp. 1014-1025, 2010.

[33] Safaei, N.,  Banjevic, D.,  Jardine, A.K., (2010). “Impact of the use-based maintenance policy on the performance of cellular manufacturing systems”, International Journal of Production Research, 48: 2233-2260.

[34] Solimanpur, M., Saeedi, S., Mahdavi, I., (2010). “Solving cell formation problem in cellular manufacturing using ant-colony-based optimization”, The International Journal of Advanced Manufacturing Technology, 50: 1135-1144.

[35] Safaei,  N., Tavakkoli-Moghaddam, R., (2009). “Integrated multi-period cell formation and subcontracting production planning in dynamic cellular manufacturing systems,” International Journal of Production Economics, 120: 301-314..

[36] Defersha, F.M., Chen, M., (2006). “A comprehensive mathematical model for the design of cellular manufacturing systems”, International Journal of Production Economics, 103: 767-783.

[37] Dantzig, G.B., (1955). “Linear programming under uncertainty”, Management science, 1: 197-206.

[38] Charnes, A., Cooper, W.W., (1959). “Chance-constrained programming”, Management science, 6: 73-79.

[39] Bertsimas, D., Thiele, A., (2006). “Robust and data-driven optimization: Modern decision-making under uncertainty”, INFORMS tutorials in operations research: models, methods, and applications for innovative decision making, 137.

[40] Bertsimas, D., Sim, M., (2004). “The price of robustness”, Operations research, 52: 35-53.