Development of a Closed-Loop Supply Chain Mathematical Model with Demand Constraints and Fuzzy Supplier Capacity and Solving it with Meta-Heuristic Algorithms

Document Type : Research/ Original/ Regular Article

Authors

1 Assistant Professor, Department of Management, Faculty of Humanities, Islamic Azad University, Ilam Branch

2 Department of Management, Faculty of Humanities, Hazrat Masoumeh University, Qom, Iran

Abstract

Nowadays, in order to achieve competitive advantages in the market, it is essential to design the supply chain network. Optimizing this network leads to efficient and effective management of the entire supply chain operation. In this article, a closed loop supply chain is designed, which is examined as multi-objective, multi-level and single product with product returns. The main goals of this issue are to minimize costs, increase profit from recycled products, increase cost savings from recycling and environmental effects. On the other hand, due to the fact that in the real world, the data related to the effective indicators in the problems are not available definitively, so it will be more appropriate to use the non-deterministic approach. In this study, the demand and capacity of the non-deterministic supplier and the approach used to solve the TH approach multi-objective model were solved and investigated using GAMS software. By increasing the size of the problem, it is impossible to solve the model with the mentioned method, so the proposed problem was solved using MOPSO and NSGA-II algorithms and the performance results of both algorithms were compared. The results show that the answers produced by the NSGA-II algorithm are of higher quality.

Keywords

Main Subjects

Smiley face

References

[1] E. Özceylan and T. Paksoy, “A mixed integer programming model for a closed-loop supply-chain network,” International Journal of Production Research, vol. 51(3), pp. 718-734, 2013.
[2] T. Paksoy, T. Bektas, and E. Ozceylan, “Operational and environmental performance measures in a multi-product closed-loop supply chain,” Transportation Research Part E 47 (4), pp. 532–546, 2011.
[3] P. Fleischmann, J. M. Beullens, and L. Bloemhof-ruwaard, Wassenhove, “The impact of product recovery on logistics network design, Production and  Operation Management,” vol. 10, pp. 156–173, 2001.
[4] D. Lee and M. Dong, “A heuristic approach to logistics network design for end-of-lease computer products recovery,” Transportation Research Part E. pp. 44 455–474, 2008.
[5] S. H .Amin and G. Zhang, “A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return,” Applied Mathematical Modeling, vol. 37(6), pp.        4165-4176, 2013.
[6] حیدری داهویی، ج.، حسینی دهشیری، س، شناسایی و اولویت بندی استراتژی‌ها برای کاهش هزینه‌های زنجیره تامین تجهیزات نیروگاهی از طریق مهندسی ارزش. مطالعات مدیریت صنعتی، دوره 17، شماره 52، ص 125-152، 1398.
[7] R. G. Yaghin, P. Sarlak, and A. Ghareaghaji, “Robust master planning of a socially responsible supply chain under fuzzy-stochastic uncertainty (A case study of clothing industry),” Engineering Applications of Artificial Intelligence, vol. 94, p. 103715, 2020.
[8] E. Yadegari, A. Alem-Tabriz, & M. Zandieh, “A memetic algorithm with a novel neighborhood search and modified solution representation for closed-loop supply chain network design,” Computers & Industrial Engineering, vol. 128, pp. 418-436, 2019.
[9] زرین پور، ن.، و امیدواری، ز،  یک مدل بهینه سازی قوی برای طراحی استراتژیک و عملیاتی زنجیره تامین نفت. مجله چشم انداز مدیریت صنعتی، دوره 10، شماره 4،  ص 155-191، 1399.
[10]  R. Tavakoli Moghadam, M. Rekavandy Omidi, and A. Ghodrat Nema, “Mathematical modeling to design integrated forward and reverse logistics network,” Management research in Iran, vol. 17, 2013.
[11] M. Ramezani, A. M. Kimiagari,  B. Karimi, and T. H. Hejazi, “Closed-loop supply chain network design under a fuzzy environment. Knowledge-Based Systems,” vol. 59, pp. 108-120, 2014.
 [12] E. Özceylan, T. Paksoy, T. Bektaş, “Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing, Transportation research part E: logistics and transportation review,” vol. 61, pp. 142-1, 2014.
[13] A. Jayant, P. Gupta, S. K. Garg, “Simulation modeling and analysis of network design for closed-loop supply chain: a case study of battery industry,” Procedia Engineering, 97(1672), pp.               2213-2221, 64, 2014.
[14] Z. Dai and X. Zheng, “Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model,” Computers & Industrial Engineering, vol. 88, pp. 444-457, 2015.
[15] Talaei, M., Moghaddam, B. F., Pishvaee, M. S., Bozorgi-Amiri, A., Gholamnejad, S., A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry, Journal of Cleaner Production, 113, P.662-673, 2016.
[16] Subulan, K., Taşan, A. S., Baykasoğlu, A., Designing an environmentally conscious tire closed-loop supply chain network with multiple recovery options using interactive fuzzy goal programming, Applied Mathematical Modeling, 39(9), P.2661-2702, 2015.
[17] N. Kafa, Y. Hani, and A. El Mhamedi, “An integrated sustainable partner selection approach with closed-loop supply chain network configuration,” IFAC-Papers Online, vol. 48(3), pp. 1840-1845, 2015.
[18] Z. G. Tao, Z. Y. Guang, S. Hao, and H. J. Song, “Multi-period closed-loop supply chain network equilibrium with carbon emission constraints. Resources, Conservation and Recycling,” vol. 104, pp. 354-365, 2015.
[19] M. Al-Salem, A. Diabat, D. Dalalah, and M. Alrefaei, “A closed-loop supply chain management problem: Reformulation and piecewise linearization, Journal of Manufacturing Systems, vol. 40, pp. 1-8, 2016.
[20] M. A. Ruimin, Y. A. O. Lifei, J. I. N. Maozhu, R. E. N. Peiyu, and L. V. Zhihan, “Robust environmental closedloop supply chain design under uncertainty,” Chaos, Solitons & Fractals, vol. 89, pp. 195-202, 2016.
[21] Kaya, O., Urek, B., A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain, Computers & Operations Research, 65, P.93-103, 2016.
[22] M. Zohal and H. Soleimani, “Developing an ant colony approach for green closed-loop supply chain network design: a case study in gold industry,” Journal of Cleaner Production, vol. 133, pp. 314-337, 2016.
[23] L. Xie and J. Ma, “Study the complexity and control of the recycling-supply chain of China's color TVs market based on the government subsidy,” Communications in Nonlinear Science and Numerical Simulation, vol. 38, pp. 102-116, 2016.
[24] A. Tiwari, P. C. Chang, M. K. Tiwari, and R. Kandhway, “A Hybrid Territory Defined evolutionary algorithm approach for closed loop green supply chain network design, Computers & Industrial Engineering, 99, pp. 432-447, 2016.
[25] D. K. Kadambala, N. Subramanian, M. K. Tiwari, M. Abdulrahman,  and C. Liu, “Closed loop supply chain networks: Designs for energy and time value efficiency,”  International Journal of Production Economics, vol. 183, pp. 382-393, 2017.
[26] S. H. Amin and F. Baki, “A facility location model for global closed-loop supply chain network designs,” Applied Mathematical Modeling, vol. 41, pp. 316-330, 2017.
[27] M. M. Paydar, V. Babaveisi, and A. S. Safaei, “An engine oil closed-loop supply chain design considering collection risk,” Computers & Chemical Engineering, vol. 104, pp. 38-55, 2017.
[28] Y. T. Chen, F. T. Chan, S. H.Chung, and W. Y. Park, “Optimization of product refurbishment in closed-loop supply chain using multi-period model integrated with fuzzy controller under uncertainties,” Robotics and Computer-Integrated Manufacturing, vol. 50, pp. 1-12, 2018.
[29] Luis J. Zeballos Carlos, A. Méndez Ana, and P. Barbosa-Povoa, “Integrating decisions of product and closed-loop supply chain design under uncertain return flows,” Computers & Chemical Engineering, vol. 112, pp. 211-238, 2018.
[30] M. Farrokh, A. Azar, G. Jandaghi, and E. Ahmadi, “A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty,” Fuzzy Sets and Systems, vol. 341, pp. 69-91, 2018.
[31] A. M. Fathollahi-Fard and M. Hajiaghaei-Keshteli, “A stochastic multi-objective model for a closed-loop supply chain with environmental considerations,” Applied Soft Computing, vol. 69, pp. 232-249, 2018.
[32] J. Ghahremani-Nahr, R. Kian, & E. Sabet, “A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm,” Expert systems with applications, vol. 116, pp. 454-471, 2019.
[33] M. S. Atabaki, M. Mohammadi, and Naderi, “B New robust optimization models for closed-loop supply chain of durable products: Towards a circular economy,” Computers & Industrial Engineering, vol. 146, p. 106520, 2020.
[34] Y. Liu, L. Ma, & Y. Liu, “A novel robust fuzzy mean-UPM model for green closed-loop supply chain network design under distribution ambiguity,” Applied Mathematical Modelling, vol. 92, pp.     99-135, 2021.
 [35] Z.Sazvar, M. Zokaee, R. Tavakkoli-Moghaddam, S. A.-S. Salari, , & S. Nayeri, “Designing a sustainable closed-loop pharmaceutical supply chain in a competitive market considering demand uncertainty,” manufacturer’s brand and waste management. Annals of Operations Research, pp. 1-32, 2021.
 [36] مقدس پور، ب، جبل عاملی، م، بزرگی امیدی، ع، ()، ارائه مدل زنجیره تامین حلقه بسته با در نظر گرفتن عوامل طرف سوم، نشریه زنجیره تامین، دوره 22، شماره 66،  ص 75-92، 1399.
[37] تاجیک جنگلی، م، ماکویی، ا، دهقانی، ا،  طراحی و برنامه‌ریزی شبکه زنجیره تامین حلقه بسته سبز روغن موتور با در نظر گرفتن سیستم گردآوری کالا و ضایعات بصورت شبکه‌ای، نشریه زنجیره تامین، دوره 22، شماره 68،  ص40-55، 1399.
[38] ملک پور کلبادی نژاد، س، سیف برقی، م،  توسعه شبکه زنجیره تامین حلقه بسته با در نظر گرفتن عوامل زیست محیطی و تصمیمات مکان یابی - موجودی در شرایط عدم اطمینان، نشریه زنجیره تامین، دوره 22، شماره 67،  ص 22- 4، 1399.
 [39] فیض اللهی، ص، حیدری، ح، توسعه مدل ریاضی شبکه زنجیره تأمین حلقه بسته چند هدفه؛ با تمرکز بر صرفه جویی هزینه‌های بازیافت در شرایط عدم اطمینان، فصلنامه مهندسی مالی و مدیریت اوراق بهادار، شماره 46، ص576- 604، 1400.
[40] M. S. Pishvaee, J. Razmi, and S. A.Torabi, “Robust possibility programming for socially responsible supply chain network design: A new approach,” Fuzzy sets and systems, vol. 206, pp. 1-20, 2012.
[41] S. A. Torabi & E. Hassini, “An interactive possibilistic programming approach for multiple objective supply chain master planning,” Fuzzy sets and systems, vol. 159(2), pp. 193-214, 2008.
[42] R. L. Haupt and S. E. Haupt, “Practical genetic algorithms,” New York: Wiley, 2, 1998.
[43] M. Clerc, “Particle Swarm Optimization,” London, British Library Cataloguing-in-Publication Data, UK, 2006.
[44] J. R. Schott, “Fault Tolerant Design Using Single and Multi criteria Genetic Algorithm Optimization (No. AFIT/CI/CIA-95-039),” Air Force Ins of Tech Wright Patterson AFB OH, 1995.
[45] Zitzler, E. Evolutionary algorithms for multi objective optimization: Methods and applications (vol. 63). Ithaca: Shaker, 1999.

Files

History

  • Receive Date: 16 August 2022
  • Revise Date: 06 May 2023
  • Accept Date: 31 May 2023
  • Publish Date: 22 June 2023

Share

How to cite

Statistics