An Approach Utilizing Epsilon-Constraint and NSGA-II for Circular Manufacturing Supply Chain Networks

محورهای موضوعی : Energy saving and waste reduction in manufacturing processes

Fatemeh Jaferi ¹ , Arash Shahin ² , Mohammadreza Vasili ³ , Omid Boyer Hassani ⁴

1 - Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Management, University of Isfahan, Isfahan, Iran
3 - Department of Industrial Engineering, Lenjan Branch, Islamic Azad University, Isfahan, Iran
4 - Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

تاریخ دریافت : 1402/02/29 تاریخ پذیرش : 1402/05/12 تاریخ انتشار : 1402/02/11

کلید واژه: NSGA-II algorithm, Optimization, epsilon-constraint, Circular Manufacturing Supply Chain,

چکیده مقاله :

Circular manufacturing supply chains offer a novel and compelling perspective within the realm of supply chain sustainability. Consequently, the development of a suitable solution approach for circular manufacturing supply chains holds significant value. This study presents appropriate solution approaches for a mathematical model that has been formulated for a circular supply chain. To address the small-sized problem, the epsilon-constraint method is proposed. This method aids in obtaining a Pareto set of optimal solutions, facilitating the evaluation of trade-offs among three objectives. Given the NP-hard nature of the problem, the non-dominated sorting genetic algorithm (NSGA-II) is employed to approximate the Pareto front for larger problem sizes. A comparative analysis is conducted between the outcomes achieved in smaller dimensions using the epsilon-constraint method and those generated by the metaheuristic algorithm. The results indicate that the error percentage of the objective function, when compared to the epsilon method, remains consistently below 1%, underscoring the effectiveness of the proposed algorithm. These methodologies empower decision-makers to offer efficient, optimal solutions, enabling them to select the most suitable alternative based on budgetary considerations and organizational policies.

منابع و مأخذ:

[1] Dastani, M., Davoodi, S. M. R., Karbassian, M., and Moeini, S. 2022. Developing a mathematical model for a green closed-loop supply chain with a multi-objective gray wolf optimization algorithm. Foundations of Computing and Decision Sciences. 47(2): 127-150.

[2] Zandkarimkhani, S., Mina, H., Biuki, M. and Govindan, K. 2020. A chance constrained fuzzy
goal programming approach for perishable pharmaceutical supply chain network
design. Ann. Oper. Res. 295 (1): 425–452.

[3] Cammarano, A., Perano, M., Michelino, F., Del Regno, C. and Caputo, M. 2022. SDGoriented supply chains: business practices for procurement and distribution. Sustainability. 14(3): 1325.

[4] Dwivedi, A., Agrawal, D., Jha, A., Gastaldi, M., Paul, S.K. and D’Adamo, I. 2021. Addressing
the challenges to sustainable initiatives in value chain flexibility: implications for Sustainable Development Goals. Global J. Flex. Syst. Manag. 22 (2): 179–197.

[5] Govindan, K., Salehian, F., Kian, H., Hosseini, S. T. and Mina, H. 2023. A location-inventory-routing problem to design a circular closed-loop supply chain network with carbon tax policy for achieving circular economy: An augmented epsilon-constraint approach. International Journal of Production Economics. 257: 108771.

[6] Nasr, A. K., Tavana, M., Alavi, B. and 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.‏

[7] MahmoumGonbadi, A., Genovese, A. and Sgalambro, A. 2021. Closed-loop supply chain design for the transition towards a circular economy: A systematic literature review of methods, applications and current gaps. Journal of Cleaner Production. 323: 129101.‏

[8] Erdoğan, A., Geçici, E. and Güler, M.G. 2023. Design of a future hydrogen supply chain: A multi-objective model for Turkey. International Journal of Hydrogen Energy. 48(31): 11775-11789.

[9] Goudarzi, Z., Seifbarghy, M. and Pishva, D. 2022. Bi-objective modeling of a closed-loop multistage supply chain considering the joint assembly center and reliability of the whole chain. Journal of Industrial and Production Engineering. 39(3): 230-252.‏

[10] Govindan, K., Nosrati-Abarghooee, S., Nasiri, M. M. and Jolai, F. 2022. Green reverse logistics network design for medical waste management: A circular economy transition through case approach. Journal of Environmental Management. 322: 115888.‏

[11] Ghasemi, P., Hemmaty, H., Pourghader Chobar, A., Heidari, M. R. and 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 (Available Online).‏

[12] Pahlevan, S. M., Hosseini, S. M. S. and Goli, A. 2021. Sustainable supply chain network design using products’ life cycle in the aluminum industry. Environmental Science and Pollution Research. 1-25 (Available Online).‏

[13] Lahri, V., Shaw, K. and Ishizaka, A. 2021. Sustainable supply chain network design problem: Using the integrated BWM, TOPSIS, possibilistic programming, and ε-constrained methods. Expert Systems with Applications. 168: 114373.‏

[14] Moslehi, M. S., Sahebi, H. and Teymouri, A. 2021. A multi-objective stochastic model for a reverse logistics supply chain design with environmental considerations. Journal of Ambient Intelligence and Humanized Computing. 12(7): 8017-8040.

[15] Fasihi, M., Tavakkoli-Moghaddam, R., Najafi, S. E. and Hajiaghaei-Keshteli, M. 2021. Developing a bi-objective mathematical model to design the fish closed-loop supply chain. International Journal of Engineering. 34(5): 1257-1268.‏

[16] Bal, A., & Badurdeen, F. 2020. A multi-objective facility location model to implement circular economy. Procedia Manufacturing. 51: 1592-1599.‏

[17] Franco, C. and Alfonso-Lizarazo, E. 2020. Optimization under uncertainty of the pharmaceutical supply chain in hospitals. Computers & Chemical Engineering. 135: 106689.‏

[18] Orjuela-Castro, J. A., Aranda-Pinilla, J. A. and Moreno-Mantilla, C. E. 2019. Identifying trade-offs between sustainability dimensions in the supply chain of biodiesel in Colombia. Computers and Electronics in Agriculture. 161: 162-169.

[19] Tavana, M., Abtahi, A. R., Di Caprio, D., Hashemi, R. and Yousefi-Zenouz, R. 2018. An integrated location-inventory-routing humanitarian supply chain network with pre-and post-disaster management considerations. Socio-Economic Planning Sciences. 64: 21-37.‏

[20] Fakhrzad, M. B.  and Lotfi, R. 2018. Green vendor managed inventory with backorder in two echelon supply chain with epsilon-constraint and NSGA-II approach. Journal of industrial engineering research in production systems. 5(11): 193-209.

[21] Perez Loaiza, R. E., Olivares‐Benitez, E., Miranda Gonzalez, P. A., Guerrero Campanur, A. and Martinez Flores, J. L. 2017. Supply chain network design with efficiency, location, and inventory policy using a multiobjective evolutionary algorithm. International Transactions in Operational Research. 24(1-2): 251-275.‏

[22] Olivares-Benitez, E., Ríos-Mercado, R. Z. and González-Velarde, J. L. 2013. A metaheuristic algorithm to solve the selection of transportation channels in supply chain design. International Journal of Production Economics. 145(1): 161-172.‏

[23] Govindan, K., Noorul Haq, A. and Devika, M. 2009. Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation. International Journal of Production Research. 47 (5): 1175-1200.

[24] Kumar, R. S., Kondapaneni, K., Dixit, V., Goswami, A., Thakur, L. S. and Tiwari, M. K. 2016.
Multi-objective modeling of production and pollution routing problem with time window:
A self-learning particle swarm optimization approach. Computers & Industrial
Engineering. 99: 29-40

[25] Haimes, Y. 1971. On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man, and Cybernetics. 3: 296-297.‏

[26] B´ erub´e, J.F., Gendreau, M. and Potvin, J.Y. 2009. An exact ∈-constraint method for biobjective combinatorial optimization problems: application to the Traveling Salesman Problem with Profits. European Journal of Operational Research. 194 (1): 39–50.

[27] Hassannayebi, E., Zegordi, S.H., Amin-Naseri, M.R. and Yaghini, M. 2017. Train timetabling
at rapid rail transit lines: a robust multi-objective stochastic programming approach. Operational Research. 17 (2): 435–477.

[28] Eskandarpour, M., Dejax, P., Miemczyk, J. and Péton, O. 2015. Sustainable supply chain network design: An optimization-oriented review. Omega. 54: 11-32.‏

[29] Liefooghe, A., Verel, S. and Hao, J.K. 2014. A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming. Applied Soft Computing. 16: pp.10-19.

[30] Devika, K., Jafarian, A. and Nourbakhsh, V. 2014. Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics ybridization techniques. European Journal of Operational Research. 235: 594–615.

[31] Govindan, K., Mina, H., Esmaeili, A. and Gholami-Zanjani, S.M. 2020. An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty. Journal of Cleaner Production. 242: 118317.

[32] Gu, X., Zhou, L., Huang, H., Shi, X. and Ieromonachou, P. 2021. Electric vehicle battery secondary use under government subsidy: a closed-loop supply chain perspective. International Journal of Production Economics. 234: 108035.

[33] Fathollahi-Fard, A. M., Ahmadi, A. and Al-e-Hashem, S. M. 2020. Sustainable closed-loop supply chain network for an integrated water supply and wastewater collection system under uncertainty. Journal of Environmental Management. 275:111277.‏

[34] Santander, P., Sanchez, F.A.C., Boudaoud, H. and Camargo, M. 2020. Closed loop supply chain network for local and distributed plastic recycling for 3D printing: a MILP-based optimization approach. Resources, Conservation and Recycling. 154: 104531.

[35] De Angelis, R., Howard, M. and Miemczyk, J. 2018. Supply chain management and the circular economy: towards the circular supply chain. Production Planning & Control. 29(6): 425-437.