Developing multi-objective mathematical model of sustainable multi-commodity, multi-level closed-loop supply chain network considering disruption risk under uncertainty

Document Type : Research Paper


1 Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

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

3 Departmant of mathematics, Noor branch, Islamic Azad University, Noor, Iran


Nowadays, the issue of the difference in core competencies has turned into the main factor of competition in the market in most organizations. In line with their operational area, the companies make decisions to further strengthen some of their capabilities, capacities, and specializations. Thus, when an organization concentrates on its strengths and makes efforts for its sustainable development, a competitive advantage evolves in the market. In this regard, the present study proposes a Multi-Objective, Multi-Level, Multi-Commodity, and Multi-Period Closed-Loop Mathematical Model for production, distribution, location, and allocation of the products. The presented model particularly aims to minimize the environmental effects and the total supply chain costs, and to control the social impacts of the supply chain. The present study is mainly innovative in the sense that it considers the quality of the manufactured and transported products, various scenarios in the closed-loop logistics as uncertainty, the capacity of the distribution and production centers, and along with the current multi-commodity discussions, considers the sustainability and resilience in the supply chain, the environmental effects in the model and minimizing the amount of the CO2 emissions. The introduced model was solved in small and medium scales using the Epsilon Constraint approach and in large scales for the case study of Sunny Plast Industries by the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) approach. The results indicated that as the demand goes up, the costs rise. Costs increase is higher in the Boom Scenario than in the Bust Scenario. Also, with the rise in demands, the number of established centers increases. This increase is faster in the Boom case.


Main Subjects

Ahmadi, S. A., & Ghasemi, P. (2022). Pricing strategies for online hotel searching: a fuzzy inference system procedure. Kybernetes, (ahead-of-print).
Babaeinesami, A., Tohidi, H., Ghasemi, P., Goodarzian, F., & Tirkolaee, E. B. (2022). A closed-loop supply chain configuration considering environmental impacts: a self-adaptive NSGA-II algorithm. Applied Intelligence, 1-19.
Chkanikova, O., & Sroufe, R. (2021). Third-party sustainability certifications in food retailing: Certification design from a sustainable supply chain management perspective. Journal of Cleaner Production, 282, 124344.
Chobar, A. P., Adibi, M. A., & Kazemi, A. (2022). Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment, Development and Sustainability, 1-28.
Dai, J., Xie, L., & Chu, Z. (2021). Developing sustainable supply chain management: The interplay of institutional pressures and sustainability capabilities. Sustainable Production and Consumption, 28, 254-268.
Diabat, A., & Jebali, A. (2021). Multi-product and multi-period closed loop supply chain network design under take-back legislation. International Journal of Production Economics, 231, 107879.
Emenike, S. N., & Falcone, G. (2020). A review on energy supply chain resilience through optimization. Renewable and Sustainable Energy Reviews, 134, 110088.
Fu, R., Qiang, Q. P., Ke, K., & Huang, Z. (2021). Closed-loop supply chain network with interaction of forward and reverse logistics. Sustainable Production and Consumption, 27, 737-752.
Ghasemi, P., Hemmaty, H., Pourghader Chobar, A., Heidari, M. R., & Keramati, M. (2022). A multi-objective and multi-level model for location-routing problem in the supply chain based on the customer’s time window. Journal of Applied Research on Industrial Engineering.
Ghomi-Avili, M., Naeini, S. G. J., Tavakkoli-Moghaddam, R., & Jabbarzadeh, A. (2018). A fuzzy pricing model for a green competitive closed-loop supply chain network design in the presence of disruptions. Journal of Cleaner Production, 188, 425-442.
Han, X., & Chen, Q. (2021). Sustainable supply chain management: Dual sales channel adoption, product portfolio and carbon emissions. Journal of Cleaner Production, 281, 125127.
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285-307.
Jabbarzadeh, A., Fahimnia, B., & Sabouhi, F. (2018). Resilient and sustainable supply chain design: sustainability analysis under disruption risks. International Journal of Production Research, 1-24.
Jahangiri, S., Abolghasemian, M., Ghasemi, P., & Pourghader Chobar, A. (2021). Simulation-based optimization: analysis of the emergency department resources under COVID-19 conditions. International journal of industrial and systems engineering, 1(1), 1504.
Javadi, M., Lotfi, M., Osório, G. J., Ashraf, A., Nezhad, A. E., Gough, M., & Catalão, J. P. (2020, July). A multi-objective model for home energy management system self-scheduling using the epsilon-constraint method. In 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) (Vol. 1, pp. 175-180). IEEE.
Kouhizadeh, M., Saberi, S., & Sarkis, J. (2021). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International Journal of Production Economics, 231, 107831.
Kumar, V., Pallathadka, H., Sharma, S. K., Thakar, C. M., Singh, M., & Pallathadka, L. K. (2022). Role of machine learning in green supply chain management and operations management. Materials Today: Proceedings, 51, 2485-2489.
Li, B., Wang, Y., & Wang, Z. (2021). Managing a closed-loop supply chain with take-back legislation and consumer preference for green design. Journal of Cleaner Production, 282, 124481.
Liu, Z., Zheng, X. X., Li, D. F., Liao, C. N., & Sheu, J. B. (2021). A novel cooperative game-based method to coordinate a sustainable supply chain under psychological uncertainty in fairness concerns. Transportation Research Part E: Logistics and Transportation Review, 147, 102237.
Moktadir, M. A., Dwivedi, A., Khan, N. S., Paul, S. K., Khan, S. A., Ahmed, S., & Sultana, R. (2021). Analysis of risk factors in sustainable supply chain management in an emerging economy of leather industry. Journal of Cleaner Production, 283, 124641.
Maadanpour Safari, F., Etebari, F., & Pourghader Chobar, A. (2021). Modelling and optimization of a tri-objective Transportation-Location-Routing Problem considering route reliability: using MOGWO, MOPSO, MOWCA and NSGA-II. Journal of optimization in industrial engineering, 14(2), 83-98.
Momenitabar, M., Dehdari Ebrahimi, Z., Arani, M., Mattson, J., & Ghasemi, P. (2022). Designing a sustainable closed-loop supply chain network considering lateral resupply and backup suppliers using fuzzy inference system. Environment, Development and Sustainability, 1-34.
Nasr, A. K., Tavana, M., Alavi, B., & Mina, H. (2021). A novel fuzzy multi-objective circular supplier selection and order allocation model for sustainable closed-loop supply chains. Journal of Cleaner Production, 287, 124994.
Negri, M., Cagno, E., Colicchia, C., & Sarkis, J. (2021). Integrating sustainability and resilience in the supply chain: A systematic literature review and a research agenda. Business Strategy and the Environment.
Paduloh, P., Djatna, T., Sukardi, S., & Muslich, M. (2020). Uncertainty models in reverse supply chain: A review. International Journal Supply Chain Management, 9(2), 139-149.
Pourghader Chobar, A., Adibi, M. A., & Kazemi, A. (2021). A novel multi-objective model for hub location problem considering dynamic demand and environmental issues. Journal of industrial engineering and management studies, 8(1), 1-31.
Pourghader Chobar, A., Sabk Ara, M., Moradi Pirbalouti, S., Khadem, M., & Bahrami, S. (2021). A multi-objective location-routing problem model for multi-device relief logistics under uncertainty using meta-heuristic algorithm. Journal of Applied Research on Industrial Engineering.
Ramadhan, G. T., Sutopo, W., & Hisjam, M. (2022). A Sustainable Location-Allocation Model for Solar-Powered Pest Control to Increase Rice Productivity. Applied System Innovation, 5(2), 39.
Rahmaty, M., Daneshvar, A., Salahi, F., Ebrahimi, M., & Chobar, A. P. (2022). Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks. Discrete Dynamics in Nature and Society, 2022.
Rezaei Kallaj, M., Abolghasemian, M., Moradi Pirbalouti, S., Sabk Ara, M., & Pourghader Chobar, A. (2021). Vehicle routing problem in relief supply under a crisis condition considering blood types. Mathematical Problems in Engineering, 2021.
Safaei, S., Ghasemi, P., Goodarzian, F., & Momenitabar, M. (2022). Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm. Environmental Science and Pollution Research, 1-15.
Sarkar, B., Sarkar, M., Ganguly, B., & Cárdenas-Barrón, L. E. (2021). Combined effects of carbon emission and production quality improvement for fixed lifetime products in a sustainable supply chain management. International Journal of Production Economics, 231, 107867.
Savaskan, R. C., Bhattacharya, S., & Van Wassenhove, L. N. (2004). Closed-loop supply chain models with product remanufacturing. Management science, 50(2), 239-252.
Shafipour-Omrani, B., Rashidi Komijan, A., Ghasemi, P., Ghasemzadeh, E., & 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.
Sohrabi, R., Pouri, K., Sabk Ara, M., Davoodi, S. M., Afzoon, E., & Pourghader Chobar, A. (2021). Applying sustainable development to economic challenges of small and medium enterprises after implementation of targeted subsidies in Iran. Mathematical Problems in Engineering, 2021.
Son, D., Kim, S., & Jeong, B. (2021). Sustainable part consolidation model for customized products in closed-loop supply chain with additive manufacturing hub. Additive Manufacturing, 37, 101643.
Sun, Y., Lin, F., & Xu, H. (2018). Multi-objective optimization of resource scheduling in Fog computing using an improved NSGA-II. Wireless Personal Communications, 102(2), 1369-1385.
Tan, Y. F., & Cao, B. Y. (2005, August). Another discussion about optimal solution to fuzzy constraints linear programming. In International Conference on Fuzzy Systems and Knowledge Discovery (pp. 156-159). Springer, Berlin, Heidelberg.
Torabzadeh, S. A., Nejati, E., Aghsami, A., & Rabbani, M. (2022). A dynamic multi-objective green supply chain network design for perishable products in uncertain environments, the coffee industry case study. International Journal of Management Science and Engineering Management, 1-18.
Tsao, Y. C., Thanh, V. V., Lu, J. C., & Yu, V. (2018). Designing sustainable supply chain networks under uncertain environments: Fuzzy multi-objective programming. Journal of Cleaner Production, 174, 1550-1565.
Wu, C. H. (2021). A dynamic perspective of government intervention in a competitive closed-loop supply chain. European Journal of Operational Research, 294(1), 122-137.
Yun, Y., Chuluunsukh, A., & Gen, M. (2020). Sustainable closed-loop supply chain design problem: A hybrid genetic algorithm approach. Mathematics, 8(1), 84.
Zavala-Alcívar, A., Verdecho, M. J., & Alfaro-Saíz, J. J. (2020). A conceptual framework to manage resilience and increase sustainability in the supply chain. Sustainability, 12(16), 6300.