Designing a sustainable integrated production system model under uncertainty considering the discount in production outsourcing costs
Subject Areas : Production PlanningSaeed Shahdoust 1 , Mohammad Fallah 2 * , Esmaeil Najafi 3
1 - 1Department of Industrial Engineering, Islamic Azad University, Central Tehran Branch
2 - Department of Industrial Engineering, Islamic Azad University, Central Tehran Branch
3 - Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Keywords: sustainable integrated production system, robust-fuzzy-probabilistic optimization method, MOGWO algorithm,
Abstract :
Sustainability in the integrated production system in supply chain networks has led to the creation of competitive advantage for companies. Therefore, companies should have proper management of their supply chain network to increase their market share. Therefore, in this article, a dual-objective model of a sustainable integrated production system is presented, taking into account the simultaneous reduction of possible costs on the system, the amount of greenhouse gas emissions, and the application of discounts on the costs of outsourcing production under uncertainty and the control of non-deterministic parameters with the robust optimization method. . Due to the NP-hard nature of the problem, the exact epsilon constraint method and meta-heuristic algorithms MOPSO, NSGA II, and MOGWO have been used to solve the model. The results of the calculations showed that the NSGA II algorithm is effective in obtaining the indicators of the number of effective answers and the distance index, and the MOPSO algorithm is also in obtaining the indicators of the most spread, the average computing time, and the distance from the ideal point, and the MOGWO algorithm is also in obtaining the averages of the first objective function . Also, the results of the implementation of the TOPSIS method for ranking the algorithms for solving the problem of a sustainable integrated production system included obtaining a desirability weight of 0.5882 for the MOPSO algorithm, obtaining a desirability weight of 0.1397 for the MOGWO algorithm, and obtaining a desirability weight of 0.7491 for the NSGA II algorithm.
[1] Alhourani, F. (2016). Cellular manufacturing system design considering machines reliability and parts alternative process routings. International Journal of Production Research, 54(3), 846-863.
[2] Arampantzi, C., & Minis, I. (2017). A new model for designing sustainable supply chain networks and its application to a global manufacturer. Journal of Cleaner Production, 156, 276-292
[3] Assid, M., Gharbi, A., & Hajji, A. (2020). Production control of failure-prone manufacturing-remanufacturing systems using mixed dedicated and shared facilities. International Journal of Production Economics, 224, 107549.
[4] 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.
[5] Chan, F. T., Li, N., Chung, S. H., & Saadat, M. (2017). Management of sustainable manufacturing systems-a review on mathematical problems. International Journal of Production Research, 55(4), 1210-1225.
[6] Chu, C. H., & Tsai, M. (1990). A comparison of three array-based clustering techniques for manufacturing cell formation. The International Journal Of Production Research, 28(8), 1417-1433.
[7] Ćwikła, G., & Foit, K. (2017). Problems of integration of a manufacturing system with the business area of a company on the example of the Integrated Manufacturing Systems Laboratory. In MATEC Web of Conferences (Vol. 94, p. 06004). EDP Sciences.
[8] Dehnavi-Arani, S., Saidi-Mehrabad, M., & Ghezavati, V. (2019). An integrated model of cell formation and scheduling problem in a cellular manufacturing system considering automated guided vehicles' movements. International Journal of Operational Research, 34(4), 542-561.
[9] Ghahremani-Nahr, J., & Ghaderi, A. (2022). Robust-fuzzy optimization approach in design of sustainable lean supply chain network under uncertainty. Computational and Applied Mathematics, 41(6), 1-44.
[10] Ghanei, S., & AlGeddawy, T. (2020). An Integrated Multi-Period Layout Planning and Scheduling Model for Sustainable Reconfigurable Manufacturing Systems. Journal of Advanced Manufacturing Systems, 19(01), 31-64.
[11] Goli, A., Tirkolaee, E. B., & Aydın, N. S. (2021). Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors. IEEE transactions on fuzzy systems, 29(12), 3686-3695.
[12] Golpîra, H., Khan, S. A. R., & Zhang, Y. (2018). Robust smart energy efficient production planning for a general job-shop manufacturing system under combined demand and supply uncertainty in the presence of grid-connected microgrid. Journal of cleaner production, 202, 649-665.
[13] Jauhari, W. A., Pujawan, I. N., & Suef, M. (2021). A closed-loop supply chain inventory model with stochastic demand, hybrid production, carbon emissions, and take-back incentives. Journal of Cleaner Production, 320, 128835.
[14] Khezri, A., Benderbal, H. H., & Benyoucef, L. (2019, September). A sustainable reconfigurable manufacturing system designing with focus on environmental hazardous wastes. In 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 317-324). IEEE.
[15] KiA, R., Javadian, N., & Tavakkoli-Moghaddam, R. (2014). A simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing system. Journal of Optimization in Industrial Engineering, 7(14), 37-52.
[16] Liu, C., Tang, D., Zhu, H., & Nie, Q. (2021). A Novel Predictive Maintenance Method Based on Deep Adversarial Learning in the Intelligent Manufacturing System. IEEE Access.
[17] Lufika, R. D., & Meutia, S. (2021, February). The integrated lean and green manufacturing system: a case study at the peeled loaf production. In IOP Conference Series: Materials Science and Engineering (Vol. 1082, No. 1, p. 012010). IOP Publishing.
[18] Mohtashami, Z., Aghsami, A., & Jolai, F. (2020). A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption. Journal of Cleaner Production, 242, 118452.
[19] Nujoom, R., Mohammed, A., & Wang, Q. (2018). A sustainable manufacturing system design: a fuzzy multi-objective optimization model. Environmental Science and Pollution Research, 25(25), 24535-24547.
[20] Ojstersek, R., Acko, B., & Buchmeister, B. (2020). Simulation study of a flexible manufacturing system regarding sustainability. Int. J. Simul. Model, 19(1), 65-76.
[21] Rabbani, M., Farrokhi-Asl, H., & Ravanbakhsh, M. (2019). Dynamic cellular manufacturing system considering machine failure and workload balance. Journal of Industrial Engineering International, 15(1), 25-40.
[22] Rajak, S., Vimal, K. E. K., Arumugam, S., Parthiban, J., Sivaraman, S. K., Kandasamy, J., & Duque, A. A. (2022). Multi-objective mixed-integer linear optimization model for sustainable closed-loop supply chain network: a case study on remanufacturing steering column. Environment, Development and Sustainability, 24(5), 6481-6507.
[23] Raoofpanah, H., Ghezavati, V., & Tavakkoli-Moghaddam, R. (2019). Applying an Imperialist competitive algorithm for scheduling parts in a green cellular manufacturing system with consideration of production planning. Journal of Industrial and Systems Engineering, 12(3), 226-248.
[24] Sadeghi, A., Suer, G., Sinaki, R. Y., & Wilson, D. (2020). Cellular manufacturing design and replenishment strategy in a capacitated supply chain system: A simulation-based analysis. Computers & Industrial Engineering, 141, 106282.
[25] Salehi-Amiri, A., Zahedi, A., Gholian-Jouybari, F., Calvo, E. Z. R., & Hajiaghaei-Keshteli, M. (2022). Designing a closed-loop supply chain network considering social factors; a case study on avocado industry. Applied Mathematical Modelling, 101, 600-631.
[26] Sarkar, B., & Bhuniya, S. (2022). A sustainable flexible manufacturing–remanufacturing model with improved service and green investment under variable demand. Expert Systems with Applications, 202, 117154.
[27] Sharda, B., & Banerjee, A. (2013). Robust manufacturing system design using multi objective genetic algorithms, Petri nets and Bayesian uncertainty representation. Journal of Manufacturing Systems, 32(2), 315-324.
[28] Shirazi, H., Kia, R., Javadian, N., & Tavakkoli-Moghaddam, R. (2014). An archived multi-objective simulated annealing for a dynamic cellular manufacturing system. Journal of Industrial Engineering International, 10(2), 1-17.
[29] Tang, S., Wang, W., & Zhou, G. (2020). Remanufacturing in a competitive market: A closed-loop supply chain in a Stackelberg game framework. Expert Systems with Applications, 113655.
[30] Tavakoli, M. M., Haleh, H., & Mohammadi, M. (2018). A mathematical model for scheduling of production process and allocation of an automatic guided vehicle in a flexible manufacturing system. International Journal of Engineering Systems Modelling and Simulation, 10(2), 125-131.
[31] Tirkolaee, E. B., Mardani, A., Dashtian, Z., Soltani, M., & Weber, G. W. (2020). A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design. Journal of Cleaner Production, 250, 119517.
[32] Utama, D. M., Santoso, I., Hendrawan, Y., & Dania, W. A. P. (2022). Integrated procurement-production inventory model in supply chain: a systematic review. Operations Research Perspectives, 100221.
[33] Yu, Z., & Khan, S. A. R. (2022). Green supply chain network optimization under random and fuzzy environment. International Journal of Fuzzy Systems, 24(2), 1170-1181.