A Multi-Objective Mathematical Model for Dynamic Cellular Manufacturing System Design under Uncertainty: A Sustainable approach

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Engineering, Sari Branch, Islamic Azad University, Sari, Iran

3 Department of Mathematics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

10.22070/jqepo.2022.15102.1209

Abstract

Using a case study in scaffold production, this paper aimed to develop an integrated multi-objective mathematical model for a dynamic and sustainable cellular manufacturing system under fuzzy uncertainty. In this field, most studies have considered only the economic component or at most two components of sustainable production. Furthermore, with increasing concerns about global warming, environmental issues have become particularly important in manufacturing products and goods. On the other hand, customer satisfaction as one of the social responsibility cases is crucial because customer satisfaction is one of the factors of sustainability of organizations and companies in a competitive environment. Accordingly, sustainable production in this article consists of three components: economic, environmental, and social responsibility. We also subjected sustainable production in a dynamic cellular manufacturing system to fuzzy uncertainty. A sustainable multi-objective mathematical model with objective functions of minimizing costs, minimizing CO2 emissions, and minimizing product shortages (customer satisfaction) was proposed. A case study on scaffolding production was solved in GAMS software with CPLEX solver and augmented Epsilon-constraint method, and its basic variables were investigated to validate the proposed model. Then, due to the high complexity of the cellular manufacturing model, the NSGA-II and MOPSO meta-heuristic algorithms were applied to solve larger problems. As the results can be shown, the NSGA-II algorithm performed better than the MOPSO algorithm. Therefore, the results are then analyzed on the NSGA-II approach. The results indicate the proper performance of the proposed solution approach

Keywords


Azadeh, A., Ravanbakhsh, M., Rezaei-Malek, M., Sheikhalishahi, M., & Taheri-Moghaddam, A. (2017). Unique NSGA-II and MOPSO algorithms for improved dynamic cellular manufacturing systems considering human factors. Applied Mathematical Modelling, 48, 655-672.
Babaeinesami, A., and Ghasemi, P. (2021). Ranking of hospitals: A new approach comparing organizational learning criteria." International Journal of Healthcare Management , 14(4),1031-1039.
Aramoon Bajestani, M., Rabbani, M., Rahimi-Vahed, A.R., & Baharian Khoshkhou, G.  (2009). A multi-objective scatter search for a dynamic cell formation problem. Computers & operations research, 36(3), 777-794.
Bayram, H., & Şahin, R. (2016). A comprehensive mathematical model for dynamic cellular manufacturing system design and Linear Programming embedded hybrid solution techniques. Computers & Industrial Engineering, 91, 10-29.
Beekaroo, D., Callychurn, D.S., & Hurreeram, D.K. (2019). Developing a sustainability index for Mauritian manufacturing companies. Ecological Indicators, 96, 250-257.
Brandon, J. (1996). Cellular Manufacturing: Integrating Technology and Management, Somerset, England: Research Studies Press LTD.
Defersha, FM. & Chen, M. (2008). A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems. International Journal of Production Research, 46(22), 6389-6413.
Drolet, J. , Marcoux, Y., & Abdulnour, G. (2008). Simulation-based performance comparison between dynamic cells, classical cells and job shops: a case study. International Journal of Production Research, 46(2), 509-536.
Duffner, F., Mauler, L., Wentker, M., Leker, J., & Winter, M. (2021). Large-scale automotive battery cell manufacturing: Analyzing strategic and operational effects on manufacturing costs. International Journal of Production Economics, 232, 107982.
Espro, C., Paone, E., Mauriello, F., Gotti, R., Uliassi, E., Bolognesi, M. L., ... & Luque, R. (2021). Sustainable production of pharmaceutical, nutraceutical and bioactive compounds from biomass and waste. Chemical Society Reviews.
Fazli-Khalaf, M., Mirzazadeh, A., & Pishvaee, M.S. (2017). A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network. Human and ecological risk assessment: an international journal, 23(8), 2119-2149.
Fesnak, A. D. (2020). The challenge of variability in chimeric antigen receptor T cell manufacturing. Regenerative engineering and translational medicine, 6(3), 322-329.
Forghani, K., Fatemi Ghomi, S.M.T., & Kia, R. (2021). Group layout design of manufacturing cells incorporating assembly and energy aspects. Engineering Optimization, 1-16.
Ghasemi, P., Mehdiabadi, A., Cristi, S., and Birau, R. (2021). Ranking of Sustainable Medical Tourism Destinations in Iran: An Integrated Approach Using Fuzzy SWARA-PROMETHEE. Sustainability 13(2), 683.
Hammad, A., Churiaque, C., Sánchez-Amaya, J. M., & Abdel-Nasser, Y. (2021). Experimental and numerical investigation of hybrid laser arc welding process and the influence of welding sequence on the manufacture of stiffened flat panels. Journal of Manufacturing Processes, 61, 527-538.
Ho, C.J.Y.H. (1989). Evaluating the impact of operating environments on MRP system nervousness. The International Journal of Production Research, 27(7), 1115-1135.
Khalilzadeh, M., Ghasemi, P., Afrasiabi, A., and Shakeri., H. (2021). Hybrid fuzzy MCDM and FMEA integrating with linear programming approach for the health and safety executive risks: a case study. Journal of Modelling in Management.
Khanchehzarrin, S., Shahmizad, M., Mahdavi, I., Mahdavi-Amiri, N., and Ghasemi, P. (2021). A model for the time dependent vehicle routing problem with time windows under traffic conditions with intelligent travel times. RAIRO--Operations Research .
Klibi, W., Ichoua, S., & Martel, A. (2013). Prepositioning emergency supplies to support disaster relief: a stochastic programming approach (Vol. 19): CIRRELT.
Liu, B., & Iwamura, K., (1998). Chance constrained programming with fuzzy parameters. Fuzzy sets and systems, 94(2), 227-237.
Machado, C.G., Winroth, M.P. and Ribeiro da Silva, E.H.D. (2020). Sustainable manufacturing in Industry 4.0: an emerging research agenda. International Journal of Production Research, 58(5), 1462-1484.
Mishra, U., Wu, J.Z., and Sarkar, B. (2020). A sustainable production-inventory model for a controllable carbon emissions rate under shortages. Journal of Cleaner Production 256, 120268.
Moldavska, A. (2016). Model-based sustainability assessment–an enabler for transition to sustainable manufacturing. Procedia Cirp, 48, 413-418.
Moore, J., Chapman, R., (1999).  Application of particle swarm to multiobjective optimization. Technical report .
Moradi, S., and Sangari, M.S. (2021). A robust optimisation approach for designing a multi-echelon, multi-product, multi-period supply chain network with outsourcing. International Journal of Logistics Systems and Management , 38(4). 488-505.
Mungwattana, A. (2000). Design of cellular manufacturing systems for dynamic and uncertain production requirements with presence of routing flexibility. Virginia Tech.  
Pagone, E., Salonitis, K., & Jolly, M. (2020). Automatically weighted high-resolution mapping of multi-criteria decision analysis for sustainable manufacturing systems. Journal of Cleaner Production, 257, 120272.
Rheault, M., Drolet, J.R, & Abdulnour, G. (1995). Physically reconfigurable virtual cells: a dynamic model for a highly dynamic environment. Computers & Industrial Engineering, 29(1-4), 221-225.
Sadeghi, A., Suer, G., Younes Sinaki, R., and Wilson, D. (2020). Cellular manufacturing design and replenishment strategy in a capacitated supply chain system: A simulation-based analysis." Computers & Industrial Engineering, 141, 106282.
Safaei, N., Saidi-Mehrabad, M., & Jabal-Ameli, M.S. (2008). A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system. European Journal of Operational Research, 185(2), 563-592.
Saidi-Mehrabad, M. & Safaei, N., (2007). A new model of dynamic cell formation by a neural approach. The International Journal of Advanced Manufacturing Technology, 33(9-10), 1001-1009.
Shafiee-Gol, S., Kia, R., Kazemi, M., Tavakkoli-Moghaddam, R., and Mostafayi Darmian, S. (2021). A mathematical model to design dynamic cellular manufacturing systems in multiple plants with production planning and location–allocation decisions. Soft Computing, 25(5), 3931-3954.
Shafipour-omran, B., Khalili-Damghani, K., and Ghasemi, P. (2020). Solving a supply chain problem using two approaches of fuzzy goal programming based on TOPSIS and fuzzy preference relations. Journal of Industrial and Systems Engineering, 13(2), 27-48.
Shafipour-Omrani, B., Rashidi Komijan, A., Ghasemi, P., Ghasemzadeh, E., and Babaeinesami, A. (2021). A simulation-optimization model for liquefied natural gas transportation considering product variety. International Journal of Management Science and Engineering Management, 16(4), 279-289.
Shahmizad, M., Khanchehzarrin, S., Mahdavi, I., and Mahdavi-Amiri, N. (2016). A Partial Delivery Bi-Objective Vehicle Routing Model with Time Windows and Customer Satisfaction Function. Mediterranean Journal of Social Sciences 7, 102-102.
Tavakkoli-Moghaddam, R., Rahimi-Vahed, A.R, Ghodratnama, A., & Siadat, A. (2009). A simulated annealing method for solving a new mathematical model of a multi-criteria cell formation problem with capital constraints. Advances in Engineering Software, 40(4), 268-273.
Tavakkoli-Moghaddam, R., Safaei, N., & Sassani, F. (2008). A new solution for a dynamic cell formation problem with alternative routing and machine costs using simulated annealing. Journal of the Operational Research Society, 59(4), 443-454.
Tavanayi, M., Hafezalkotob, A., & Valizadeh, J. (2021). Cooperative cellular manufacturing system: A cooperative game theory approach. Scientia Iranica, 28(5), 2769-2788.
Zhao, C., & Wu, Z. (2000). A genetic algorithm for manufacturing cell formation with multiple routes and multiple objectives. International journal of production research, 38(2), 385-395.
Zheng, J., Zhou, X., Yu, Y., Wu, J., Ling, W., & Ma, H. (2020). Low carbon, high efficiency and sustainable production of traditional manufacturing methods through process design strategy: Improvement process for sand casting defects. Journal of Cleaner Production, 253, 119917.