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Developing a Mathematical Model for a Green Closed-Loop Supply Chain with a Multi-Objective Gray Wolf Optimization Algorithm

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Intense competition in today’s market and quick change in customer preferences, along with the rapid development of technology and globalization, have forced companies to work as members of a supply chain instead of individual companies. The success of the supply chain depends on the integration and coordination of all its institutions to form an efficient network structure. An efficient network leads to cost savings throughout the supply chain and helps it respond to customer needs faster. Accordingly, and with respect to the importance of the supply chain, in this study a developed mathematical model for the design of a green closed-loop supply chain is presented. In this mathematical model, the economic and environmental objectives are simultaneously optimized. In order to tackle this mathematical model, two methods of epsilon constraint and multi-objective gray wolf optimization (MOGWO) algorithm have been applied. The results of comparisons between the two mentioned methods show that MOGWO reduce the average solving time from about 1300 seconds to 88 seconds. In the last step of this research, in order to show the application of the proposed mathematical model and the method of solving the research problem, it was implemented in the supply chain of Dalan Kouh diary product and the Pareto optimal solutions were analyzed.
Rocznik
Strony
127--150
Opis fizyczny
Bibliogr. 49 poz., rys., tab.
Twórcy
  • Isfahan (Khorasgan) Branch Islamic Azad University, Isfahan, Iran
  • Dehaghan branch Islamic Azad University, Dehaghan, Iran
  • Malek Ashtar University of Technology, Isfahan, Iran
  • University of Isfahan, Isfahan, Iran
Bibliografia
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  • [5] Babaee Tirkolaee, E., Goli, A., Pahlevan, M., & Malekalipour Kordestanizadeh, R. (2019). A robust bi-objective multi-trip periodic capacitated arc routing problem for urban waste collection using a multi-objective invasive weed optimization. Waste Management & Research, 37(11), 1089-1101.
  • [6] Batista, L., Gong, Y., Pereira, S., Jia, F., & Bittar, A. (2018). Circular supply chains in emerging economies–a comparative study of packaging recovery ecosystems in China and Brazil. International Journal of Production Research, 1-21.
  • [7] Beheshtinia, A. (2017). Presenting a Genetic Algorithm for the Problem of Vehicle Routing Integrity and Production Timing in the Supply Chain (Case Study: Medical Supply Chain). Industrial Engineering Journal, 51 (2), 147-160.
  • [8] Bressanelli, G., Perona, M., & Saccani, N. (2018). Challenges in supply chain redesign for the Circular Economy: a literature review and a multiple case study. International Journal of Production Research, 1-21.
  • [9] Davoodi, S. M. R., & Goli, A. (2019). An integrated disaster relief model based on covering tour using hybrid Benders decomposition and variable neighborhood search: Application in the Iranian context. Computers & Industrial Engineering, 130, 370-380.
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  • [12] Fahimnia, B., Davarzani, H., & Eshragh, A. (2018). Planning of complex supply chains: A performance comparison of three meta-heuristic algorithms. Computers & Operations Research, 89, 241-252.
  • [13] Farooque, M., Zhang, A., Thurer, M., Qu, T., & Huisingh, D. (2019). Circular supply chain management: A definition and structured literature review. Journal of Cleaner Production.
  • [14] 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.
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  • [16] Garza-Reyes, J. A., Salomé Valls, A., Peter Nadeem, S., Anosike, A., & Kumar, V. (2018). A circularity measurement toolkit for manufacturing SMEs. International Journal of Production Research, 1-25.
  • [17] Ghorbanpour, P., & Shamsodin, N.(2016). Designing a Structural Model for Green Supply Chain Management Actions Using Fuzzy Interpretative Structural Modeling Approach. Investigating operations in its applications, 13.
  • [18] Goli, A., Babaee Tirkolaee, E., & Soltani, M. (2019). A robust just-in-time flow shop scheduling problem with outsourcing option on subcontractors. Production & Manufacturing Research, 7(1), 294-315.
  • [19] Goli, A., & Davoodi, S. M. R. (2018). Coordination policy for production and delivery scheduling in the closed loop supply chain. Production Engineering, 12(5), 621-631.
  • [20] Goli, A., & Malmir, B. (2020). A covering tour approach for disaster relief locating and routing with fuzzy demand. International Journal of Intelligent Transportation Systems Research, 18(1), 140-152.
  • [21] Goli, A., Khademi Zareh, H., Tavakkoli-Moghaddam, R., & Sadeghieh, A. (2018). A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry. Journal of Industrial and Systems Engineering, 11(4), 190-203.
  • [22] Goli, A., Zare, H. K., Tavakkoli-Moghaddam, R., & Sadeghieh, A. (2019). Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm. Numerical Algebra, Control & Optimization, 9(2), 187.
  • [23] Goli, A., Zare, H. K., Tavakkoli-Moghaddam, R., & Sadegheih, A. (2019). Multiobjective fuzzy mathematical model for a financially constrained closed-loop supply chain with labor employment. Computational Intelligence.
  • [24] Goli, A., Zare, H. K., Tavakkoli-Moghaddam, R., & Sadeghieh, A. (2019). Hybrid artificial intelligence and robust optimization for a multi-objective product portfolio problem Case study: The dairy products industry. Computers & Industrial Engineering, 137, 106090.
  • [25] Golpîra, H., Zandieh, M., Najafi, E., Sadi-Nezhad, S. (2017). A multiobjective, multi-echelon green supply chain network design problem with risk-averse retailers in an uncertain environment. Scientia Iranica. Transaction E: Industrial Engineering, 24(1), 413-423.
  • [26] Goodarzian, F., Wamba, S. F., Mathiyazhagan, K., & Taghipour, A. (2021). A new bi-objective green medicine supply chain network design under fuzzy environment: Hybrid metaheuristic algorithms. Computers & Industrial Engineering, 160, 107535.
  • [27] Hassani, A. (2010). Design of the supply chain outstanding corrosive goods. Master Thesis for Industrial Engineering, The trend of industries, Tarbiat Modares University.
  • [28] Howard, M., Hopkinson, P., & Miemczyk, J. (2019). The regenerative supply chain: a framework for developing circular economy indicators. International Journal of Production Research, 57(23), 7300-7318.
  • [29] Kirchherr, J., Reike, D., & Hekkert, M. (2017). Conceptualizing the circular economy: An analysis of 114 definitions. Resources, Conservation and Recycling, 127, 221-232.
  • [30] Kumar, V., Sezersan, I., Garza-Reyes, J. A., & AL-Shboul, M. A. (2018). Circular economy in the manufacturing sector: Benefits, opportunities and barriers. Management Decision. (In press)
  • [31] Liang, L., & Kouesta, H. J. (2018). Green Design of a Cellulosic Butanol Supply Chain Network: A Case Study of Sorghum Stem Bio-butanol in Missouri. BioResources, 13(3), 5617-5642.
  • [32] Mangla, S. K., Luthra, S., Mishra, N., Singh, A., Rana, N. P., Dora, M., & Dwivedi, Y. (2018). Barriers to effective circular supply chain management in a developing country context. Production Planning & Control, 29(6), 551-569.
  • [33] Miranda-Ackerman, M. A., Azzaro-Pantel, C., & Aguilar-Lasserre, A. A. (2017). A green supply chain network design framework for the processed food industry: Application to the orange juice agrofood cluster. Computers & Industrial Engineering, 109, 369-389.
  • [34] Mortazavi, S., & Seyfbarghi, M., (2018). Two-objective modeling of allocation problem in a green supply chain considering the transport system and CO2 emissions. Industrial Management Outlook. 29, 163-185.
  • [35] Murray, A., Skene, K., & Haynes, K. (2017). The circular economy: An interdisciplinary exploration of the concept and application in a global context. Journal of Business Ethics, 140(3), 369-380.
  • [36] Nouridarian, M. Taleezadeh, A. (2018). Developing a model of economic production in integrated and non-integrated level supply chains, taking into account the optimal inventory control policy. Industrial Engineering Journal, 52 (1), 125-137.
  • [37] Nurjanni, K. P., Carvalho, M. S., & Costa, L. (2017). Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, 183, 421-432.
  • [38] Pasuki, T., Çalik, A., Kumpf, A., & Weber, G. W. (2019). A New Model for Lean and Green Closed-Loop Supply Chain Optimization. In Lean and Green Supply Chain Management (pp. 39-73). Springer, Cham.
  • [39] Rad, R. S., & Nahavandi, N. (2018). A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. Journal of Cleaner Production.
  • [40] Reike, D., Vermeulen, W. J., & Witjes, S. (2018). The circular economy: New or Refurbished as CE 3.0? - Exploring Controversies in the Conceptualization of the Circular Economy through a Focus on History and Resource Value Retention Options. Resources, Conservation and Recycling, 135, 246-264.
  • [41] Saffar, M. Shakouriganjavi, H. Razmi, GH. (2017). Designing a Supply Chain Network Considering Environmental Factors under Uncertainty and Solving It with Multi-objective Differential Evolutionary Algorithms (MODE). Journal of Environmental Science and Technology, 19, 209-221.
  • [42] Sangaiah, A. K., Tirkolaee, E. B., Goli, A., & Dehnavi-Arani, S. (2019). Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem. Soft Computing, 1-21.
  • [43] Su, B., Heshmati, A., Geng, Y., & Yu, X. (2013). A review of the circular economy in China: moving from rhetoric to implementation. Journal of Cleaner Production, 42, 215-227.
  • [44] Tarokh, M.& Gouke, M. (2010). An overall model for optimizing reverse logistics network design with uncertainty. Industrial Engineering Journal of Industrial Engineering, 1392-193-159.
  • [45] Tirkolaee, E. B., Alinaghian, M., Hosseinabadi, A. A. R., Sasi, M. B., & Sangaiah, A. K. (2019a). An improved ant colony optimization for the multi-trip Capacitated Arc Routing Problem. Computers & Electrical Engineering, 77, 457-470.pply Chain Management (pp. 39-73). Springer, Cham.
  • [46] Tirkolaee, E. B., Goli, A., & Weber, G. W. (2019b). Multi-objective Aggregate Production Planning Model Considering Overtime and Outsourcing Options Under Fuzzy Seasonal Demand. In Advances in Manufacturing II (pp. 81-96). Springer, Cham.
  • [47] Tirkolaee, E. B., Hosseinabadi, A. A. R., Soltani, M., Sangaiah, A. K., & Wang, J. (2018). A hybrid genetic algorithm for multi-trip green capacitated arc routing problem in the scope of urban services. Sustainability, 10(5), 1366.
  • [48] Zhang, H., & Yang, K. (2018). Multi-Objective Optimization for Green Dual-Channel Supply Chain Network Design Considering Transportation Mode Selection. International Journal of Information Systems and Supply Chain Management (IJISSCM), 11(3), 1-21.
  • [49] Zhuo, H., & Wei, S. (2017). Gaming of green supply chain members under government subsidies—based on the perspective of demand uncertainty. In Proceedings of the Tenth International Conference on Management Science and Engineering Management (pp. 1105-1116). Springer, Singapore.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-25f03286-0f4e-45dc-a097-8d941d333bd9
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