Multi-objective Green Supply Chain Network Design Considering Carbon Emissions and Electricity Consumption | ||
| Journal of Engineering Management and Soft Computing | ||
| مقاله 3، دوره 12، شماره 1 - شماره پیاپی 22، فروردین 2026، صفحه 14-35 اصل مقاله (609.85 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2026.14628.1326 | ||
| نویسندگان | ||
| Mohammad Reza Fathi1؛ Amirhossein Saffarinia* 2؛ Mohammad Javad Pahlevanzadeh2 | ||
| 1Associate Professor, Department of Management and Accounting, College of Farabi, University of Tehran, Iran | ||
| 2Department of Management and Accounting, College of Farabi, University of Tehran, Iran | ||
| چکیده | ||
| This study aims to design a green closed-loop supply chain network (GCLSCN) for battery products, balancing economic efficiency, environmental responsibility, and timely customer delivery. The main objectives include minimizing total cost, energy consumption, and carbon emissions. A multi-objective mathematical model is developed, covering suppliers, production plants, distribution centers, customers, collection centers, recycling units, and disposal facilities with different capacities and technologies. To address the problem, two multi-objective metaheuristic algorithms, NSGA-II and NRGA, are applied and compared based on solution diversity, closeness to the ideal solution, Pareto set size, and computation time. Demand uncertainty is included to better reflect real conditions. Results show that NSGA-II provides a broader and more diverse set of solutions, offering decision-makers greater flexibility, while NRGA delivers better computational efficiency. Considering variable capacities and uncertain demand improves the model’s practicality and robustness. Overall, this research presents a comprehensive framework for sustainable closed-loop supply chain design in the battery industry and offers useful insights for practitioners, while supporting future studies involving dynamic modeling and advanced uncertainty techniques. | ||
| کلیدواژهها | ||
| Green closed-loop supply chain؛ Network design؛ Metaheuristic algorithm؛ multi-objective optimization؛ Supply chain management | ||
| مراجع | ||
|
Chai, J., Qian, Z., Wang, F., & Zhu, J. (2021). Process innovation for green product in a closed loop supply chain with remanufacturing. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03888-y
Chiappetta Jabbour, C. J., Mauricio, A. L., & de Sousa Jabbour, A. B. L. (2017). Critical success factors and green supply chain management proactivity: Shedding light on the human aspects of this relationship based on cases from the Brazilian industry. Production Planning and Control, 28(6–8), 671–683. https://doi.org/10.1080/09537287.2017.1309705
D’Adamo, I., Gastaldi, M., & Ozturk, I. (2023). The sustainable development of mobility in the green transition: Renewable energy, local industrial chain, and battery recycling. Sustainable Development, 31(2). https://doi.org/10.1002/sd.2424
da Silva Lima, L., Cocquyt, L., Mancini, L., Cadena, E., & Dewulf, J. (2023). The role of raw materials to achieve the Sustainable Development Goals: Tracing the risks and positive contributions of cobalt along the lithium-ion battery supply chain. Journal of Industrial Ecology, 27(3). https://doi.org/10.1111/jiec.13336
De Sousa Jabbour, A. B. L., & De Souza, C. L. (2015). Opportunities and challenges for dealing with barriers to the adoption of green supply chain management practices: Guidelines based on multiple-case studies in Brazil | Oportunidades e desafios para lidar com as barreiras à adoção de práticas de green sup. Gestao e Producao, 22(2), 295–310. https://doi.org/10.1590/0104-530X871-13
Etemad, M., Nezafati, N., & Fathi, M. R. (2021). Designing a green closed-loop supply chain network considering customer relationship management and solving it using a multi-objective genetic algorithm. Journal of Environmental Science and Technology. https://doi.org/10.22034/jest.2021.45374.4729
Faris, H. M., & Maan, H. Y. (2020). Evaluation of green supply chain management practices under uncertainty environment: Case study in the company for batteries industry. IOP Conference Series: Materials Science and Engineering, 881(1). https://doi.org/10.1088/1757-899X/881/1/012085
Fathi, M. R., Amani, L., & Torabi, M. (2025). Futures studies of biodiesel energy in Iran with a scenario planning approach. Journal of Sustainable Energy Systems, 4(4), 383–403.
Fathi, M. R., Azar, A., & Jandaghi, G. R. (2022). Energy carrier consumption optimization policy in National Iranian Copper Industries Company (Case study: Sarcheshmeh Copper Complex). Public Policy in Administration, 1(45), 89.
Fathi, M. R., Jafarnejad Chaghooshi, A., & Safari, H. (2019). A fuzzy multi-objective mathematical programming model for a closed-loop supply chain network design by considering environmental factors. Journal of Environmental Science and Technology, 21(8), 65–76.
Fathi, M. R., & Jandaghi, G. R. (2022). Mathematical modeling of tire’s multi-objective supply chain network at uncertainty circumstances and solving it using meta-heuristic algorithms. Logistics Thought Quarterly, 21(82), 157–184.
Fathi, M. R., Khaleghverdi, N., Raeesi Nafchi, S. R., & Sobhani, S. M. (2024). Structuring the affecting indicators on the sustainable food supply chain performance based on fuzzy cognitive mapping and fuzzy DEMATEL. Fuzzy Optimization and Modeling Journal, 5(3), 67–87.
Fathi, M. R., Pahlevanzadeh, M., & Saffarinia, A. (2024). Designing a sustainable closed-loop supply chain network of automobile tires using a multi-objective mathematical programming approach: A case study. Green Development Management Studies, 3(1), 223–244.
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, 23(8), 2119–2149. https://doi.org/10.1080/10807039.2017.1367644
Ferreira, M. A., Jabbour, C. J. C., & de Sousa Jabbour, A. B. L. (2017). Maturity levels of material cycles and waste management in a context of green supply chain management: An innovative framework and its application to Brazilian cases. Journal of Material Cycles and Waste Management, 19(1), 516–525. https://doi.org/10.1007/s10163-015-0416-5
Ghalandari, M., Amirkhan, M., & Amoozad-Khalili, H. (2023). A hybrid model for robust design of sustainable closed-loop supply chain in lead-acid battery industry. Environmental Science and Pollution Research, 30(1). https://doi.org/10.1007/s11356-022-21840-4
He, Q. R., & Chen, P. K. (2022). Developing a green supplier evaluation system for the chinese electric vehicle battery manufacturing industry based on supplier willingness to participate in green collaboration. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2022.3205155
Jiménez López, M., Arenas Parra, M. del M., Bilbao Terol, A. M., & Rodríguez Uría, M. V. (2007). Linear programming with fuzzy parameters: An interactive method resolution. European Journal of Operational Research, 177(3), 1599–1609. https://doi.org/10.1016/j.ejor.2005.10.002
Kumar, P., Singh, R. K., Paul, J., & Sinha, O. (2021). Analyzing challenges for sustainable supply chain of electric vehicle batteries using a hybrid approach of Delphi and Best-Worst Method. Resources, Conservation and Recycling, 175, 105879. https://doi.org/10.1016/J.RESCONREC.2021.105879
Mauricio, A. L., & De Sousa Jabbour, A. B. L. (2017). Critical success factors for GSCM adoption: Case studies in the automotive battery industry. Gestao e Producao, 24(1), 78–94. https://doi.org/10.1590/0104-530X2267-16
Mokhlesabadi, Sh., Kabaranzadeh Ghadim, M., Aghajani Kasegar, D. H., & Mohebbi, M. M. (2021). Identification of key performance indicators and evaluation of green closed-loop supply chain network with fuzzy DANP approach. Journal of Executive Management Research, 13(26), 221–253. https://doi.org/10.22080/jem.2021.19074.3243
Nasrollahi, M., Fathi, M. R., Sanouni, H. R., Sobhani, S. M., & Behrooz, A. (2020). Impact of coercive and non-coercive environmental supply chain sustainability drivers on supply chain performance: Mediation role of monitoring and collaboration. International Journal of Sustainable Engineering, 14(2), 98–106.
Niranjan, T., Thanigaivelan, R., & Singaravel, B. (2022). Analysis of a multi-channel closed loop green supply chain using modified particle swarm optimization algorithm. In Lecture Notes in Mechanical Engineering. https://doi.org/10.1007/978-981-16-7282-8_60
Rajaeifar, M. A., Ghadimi, P., Raugei, M., Wu, Y., & Heidrich, O. (2022). Challenges and recent developments in supply and value chains of electric vehicle batteries: A sustainability perspective. Resources, Conservation and Recycling, 180, 106144. https://doi.org/10.1016/J.RESCONREC.2021.106144
Ren, Z., Li, H., Yan, W., Lv, W., Zhang, G., Lv, L., Sun, L., Sun, Z., & Gao, W. (2023). Comprehensive evaluation on production and recycling of lithium-ion batteries: A critical review. Renewable and Sustainable Energy Reviews, 185, 113585. https://doi.org/10.1016/J.RSER.2023.113585
Seles, B. M. R. P., de Sousa Jabbour, A. B. L., Jabbour, C. J. C., & Dangelico, R. M. (2016). The green bullwhip effect, the diffusion of green supply chain practices, and institutional pressures: Evidence from the automotive sector. International Journal of Production Economics, 182, 342–355. https://doi.org/10.1016/j.ijpe.2016.08.033
Sherif, S. U., Asokan, P., Sasikumar, P., Mathiyazhagan, K., & Jerald, J. (2021). Integrated optimization of transportation, inventory and vehicle routing with simultaneous pickup and delivery in two-echelon green supply chain network. Journal of Cleaner Production, 287. https://doi.org/10.1016/j.jclepro.2020.125434
Tseng, M. L., & Chiu, A. S. F. (2012). Grey-Entropy analytical network process for green innovation practices. Procedia - Social and Behavioral Sciences, 57. https://doi.org/10.1016/j.sbspro.2012.09.1152
Yang, Z., Huang, H., & Lin, F. (2022). Sustainable electric vehicle batteries for a sustainable world: Perspectives on battery cathodes, environment, supply chain, manufacturing, life cycle, and policy. Advanced Energy Materials, 12(26). https://doi.org/10.1002/aenm.202200383
Zhu, Q., & Sarkis, J. (2006). An inter-sectoral comparison of green supply chain management in China: Drivers and practices. Journal of Cleaner Production, 14(5). https://doi.org/10.1016/j.jclepro.2005.01.003
Zhu, Q., Sarkis, J., & Lai, K. hung. (2007). Green supply chain management: Pressures, practices and performance within the Chinese automobile industry. Journal of Cleaner Production, 15(11–12). https://doi.org/10.1016/j.jclepro.2006.05.021
Jafari, M., Akhavan, P., & Akbari, A. H. (2026). Enhancing supply chain agility and performance through big data analytics: the role of digitalization and top management support. International Journal of Productivity and Performance Management, 1-22. https://doi.org/10.1108/IJPPM-06-2025-0557
Tavakkoli-Moghaddam, R., Akbari, A. H., Tanhaeean, M., Moghdani, R., Gholian-Jouybari, F., & Hajiaghaei-Keshteli, M. (2024). Multi-objective boxing match algorithm for multi-objective optimization problems. Expert Systems with Applications, 239, 122394. https://doi.org/10.1016/j.eswa.2023.122394
Yavari, M., & Akbari, A. H. (2023). Service level and profit maximisation in order acceptance and scheduling problem with weighted tardiness. International Journal of Industrial and Systems Engineering, 43(3), 331-362. https://doi.org/10.1504/IJISE.2023.129138 | ||
|
آمار تعداد مشاهده مقاله: 90 تعداد دریافت فایل اصل مقاله: 117 |
||