A multi-objective multi-echelon closed-loop supply chain with disruption in the centers | ||
| Journal of Quality Engineering and Production Optimization | ||
| دوره 6، شماره 2، اسفند 2021، صفحه 31-58 اصل مقاله (1.03 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22070/jqepo.2021.14668.1192 | ||
| نویسندگان | ||
| Kaveh keshmiry zadeh1؛ Fatemeh Harsej* 2؛ Mahboubeh Sadeghpour3؛ Mohammad Molani Aghdam4 | ||
| 1Department of Industrial Engineering , Nour Branch, Islamic Azad University, Nour, Iran | ||
| 2Department of Industrial Engineering and Quality Research Centre, Nour Branch, Islamic Azad University, Nour, Iran | ||
| 3Department of Industrial Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran | ||
| 4Innovation and Management Research Center, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran | ||
| چکیده | ||
| – Supply chain management has significant economic and environmental effects, including strategic, tactical, and operational decisions. According to the need for further cost reduction and improving the process of the organization in the direction of customer demand, the concept of the supply chain has become increasingly important, and the organizations seek to expand this concept within their organizational framework. In this regard, efficient planning of products distribution in the supply chain considering disruption is very important. Thus, this study develops a multi-objective mixed-integer programming mathematical model to design a green multi-echelon closed-loop supply chain with the possibility of disruptions. Furthermore, the ε-constraint method is applied to solve and validate the proposed model in small-scale problems. On the other hand, a non-dominated sorting genetic algorithm is developed for solving large-sized problems. Results indicate that the proposed model has performed well in obtaining optimal solutions, and the proposed algorithm has an efficient performance. | ||
| کلیدواژهها | ||
| closed-loop supply chain؛ green supply chain؛ customer satisfaction؛ ε-constraint method؛ NSGA-II | ||
| مراجع | ||
|
Alegoz, M., Kaya, O., & Bayindir, Z. P. (2020). Closing the loop in supply chains: Economic and environmental effects. Computers & Industrial Engineering, 142, 106366. Atoei, F., Teimory, E., & Amiri, A. (2013). Designing reliable supply chain network with disruption risk. International Journal of Industrial Engineering Computations, 4(1), 111-126. Azad, N., Saharidis, G. K., Davoudpour, H., Malekly, H., & Yektamaram, S. A. (2013). Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach. Annals of Operations Research, 210(1), 125-163. Banasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. (2017). Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain. International Journal of Production Economics, 183, 409-420. Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. In Das summa summarum des management (pp. 265-275). Gabler. Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems (Vol. 5, pp. 79-104). New York: Springer. Darbari, J. D., Kannan, D., Agarwal, V., & Jha, P. C. (2019). Fuzzy criteria programming approach for optimising the TBL performance of closed loop supply chain network design problem. Annals of Operations Research, 273(1), 693-738. Diabat, A., Jabbarzadeh, A., & Khosrojerdi, A. (2019). A perishable product supply chain network design problem with reliability and disruption considerations. International Journal of Production Economics, 212, 125-138. Dondo, R. G., & Méndez, C. A. (2016). Operational planning of forward and reverse logistic activities on multi-echelon supply-chain networks. Computers & chemical engineering, 88, 170-184. Guo, J., Chopra, H., & Gen, M. (2020). Research on green closed-loop supply chain with the consideration of double subsidy in e-commerce environment. Computers & Industrial Engineering, 149, 106779. Haddadsisakht, A., & Ryan, S. M. (2018). Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax. International journal of production economics, 195, 118-131. Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation Research Part E: Logistics and Transportation Review, 134, 101764. Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation Research Part E: Logistics and Transportation Review, 87, 20-52. Hatefi, S. M., & Jolai, F. (2015). Reliable forward-reverse logistics network design under partial and complete facility disruptions. International Journal of Logistics Systems and Management, 20(3), 370-394. Hatefi, S. M., Jolai, F., Torabi, S. A., & Tavakkoli-Moghaddam, R. (2015). A credibility-constrained programming for reliable forward–reverse logistics network design under uncertainty and facility disruptions. International Journal of Computer Integrated Manufacturing, 28(6), 664-678. Hatefi, S. M., & Jolai, F. (2014). Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions. Applied mathematical modelling, 38(9-10), 2630-2647. Hasanov, P., Jaber, M. Y., & Tahirov, N. (2019). Four-level closed loop supply chain with remanufacturing. Applied Mathematical Modelling, 66, 141-155. Ivanov, D., Pavlov, A., Pavlov, D., & Sokolov, B. (2017). Minimization of disruption-related return flows in the supply chain. International Journal of Production Economics, 183, 503-513. Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & industrial engineering, 116, 178-191. Jamshidi, R., Ghomi, S. F., & Karimi, B. (2012). Multi-objective green supply chain optimization with a new hybrid memetic algorithm using the Taguchi method. Scientia Iranica, 19(6), 1876-1886. Krajčovič, M., Hančinský, V., Dulina, Ľ., Grznár, P., Gašo, M., & Vaculík, J. (2019). Parameter setting for a genetic algorithm layout planner as a toll of sustainable manufacturing. Sustainability, 11(7), 2083. Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465. Mohammed, F., Selim, S. Z., Hassan, A., & Syed, M. N. (2017). Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transportation Research Part D: Transport and Environment, 51, 146-172. Mohammed, F., Hassan, A., & Selim, S. Z. (2018). Robust optimization for closed-loop supply chain network design considering carbon policies under uncertainty. International Journal of Industrial Engineering, 25(4), 526-558. Nayeri, S., Paydar, M. M., Asadi-Gangraj, E., & Emami, S. (2020). Multi-objective fuzzy robust optimization approach to sustainable closed-loop supply chain network design. Computers & Industrial Engineering, 148, 106716. Niknejad, A., & Petrovic, D. (2014). Optimisation of integrated reverse logistics networks with different product recovery routes. European Journal of Operational Research, 238(1), 143-154. Ramezani, M., Kimiagari, A. M., & Karimi, B. (2014). Closed-loop supply chain network design: A financial approach. Applied Mathematical Modelling, 38(15-16), 4099-4119. Sadeghi, B., Rostami, A., & Momeni, S. S. (2015). Facile green synthesis of silver nanoparticles using seed aqueous extract of Pistacia atlantica and its antibacterial activity. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 134, 326-332. Salehi-Amiri, A., Zahedi, A., Akbapour, N., & Hajiaghaei-Keshteli, M. (2021). Designing a sustainable closed-loop supply chain network for walnut industry. Renewable and Sustainable Energy Reviews, 141, 110821. Santoso, T., Ahmed, S., Goetschalckx, M., & Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167(1), 96-115. Soleimani, H., & Kannan, G. (2015). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling, 39(14), 3990-4012. Teimuory, E., Atoei, F., Mohammadi, E., & Amiri, A. (2013). A multi-objective reliable programming model for disruption in supply chain. Management Science Letters, 3(5), 1467-1478. Torabi, S. A., Namdar, J., Hatefi, S. M., & Jolai, F. (2016). An enhanced possibilistic programming approach for reliable closed-loop supply chain network design. International Journal of Production Research, 54(5), 1358-1387. Yavari, M., & Zaker, H. (2020). Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks. Computers & Chemical Engineering, 134, 106680. Yu, H., & Solvang, W. D. (2016). A stochastic programming approach with improved multi-criteria scenario-based solution method for sustainable reverse logistics design of waste electrical and electronic equipment (WEEE). Sustainability, 8(12), 1331. Yu, H., & Solvang, W. D. (2020). A fuzzy-stochastic multi-objective model for sustainable planning of a closed-loop supply chain considering mixed uncertainty and network flexibility. Journal of Cleaner Production, 266, 121702. Zarbakhshnia, N., Soleimani, H., Goh, M., & Razavi, S. S. (2019). A novel multi-objective model for green forward and reverse logistics network design. Journal of Cleaner Production, 208, 1304-1316. Zhang, X., Zhao, G., Qi, Y., & Li, B. (2019). A robust fuzzy optimization model for closed-loop supply chain networks considering sustainability. Sustainability, 11(20), 5726. | ||
|
آمار تعداد مشاهده مقاله: 303 تعداد دریافت فایل اصل مقاله: 241 |
||