A NOVEL BI-OBJECTIVE MODEL FOR A MULTI-PERIOD MULTI-PRODUCT CLOSED-LOOP SUPPLY CHAIN

An Erratum to this article was published on March 27, 2023. https://dergipark.org.tr/en/pub/jesd/article/1272960

Abstract

Closed-loop supply chain (CLSC) is a kind of supply chain which contains forward and backward flows of commodities within a logistics network. In the decision-making process of CLSC, locational, inventory control and transportation issues are addressed to deal with strategic, tactical and operational decisions. This paper utilizes a novel bi-objective mixed-integer linear programming (MILP) model to formulate a multi-period multi-product CLSC design problem considering aggregate cost minimization and service level maximization at the same time. To tackle the bi-objectiveness of the model, goal attainment method (GAM) is applied which is then executed by Gurobi Python API to test the applicability of the suggested model for three different scales (small, medium and large). It is demonstrated that the proposed methodology can find the optimal solutions for different problems in a maximum of 500 seconds. Finally, a set of sensitivity analyses is carried out on the main parameters in order to test the behaviors of the objective functions and suggest managerial insights as well as decision aids. The results reveal that the model is highly dependent on the demand parameter, that is, an increase in demand is closely related to an increase in the aggregate cost and a simultaneous downward trend in the service level.

Keywords

• Closed-Loop Supply Chain
• Multi-Objective Model
• Mixed-Integer Model
• Goal Attainment Method
• Sensitivity Analysis

ÇOK PERİYOTLU ÇOK ÜRÜNLÜ KAPALI DÖNGÜ TEDARİK ZİNCİRİ İÇİN YENİ BİR ÇİFT-AMAÇLI MODEL

An Erratum to this article was published on March 27, 2023. https://dergipark.org.tr/en/pub/jesd/article/1272960

Abstract

Kapalı döngü tedarik zinciri (KDTZ), bir lojistik ağ içinde ürünlerin ileri ve geri akışlarını içeren bir tür tedarik zinciridir. KDTZ'nin karar verme sürecinde, stratejik, taktik ve operasyonel kararlarla başa çıkmak için lokasyon, envanter kontrolü ve taşıma konuları ele alınmaktadır. Bu araştırma, aynı anda hem toplam maliyet minimizasyonu hem de hizmet seviyesi maksimizasyonu dikkate alınarak çok periyotlu ve çok ürünlü bir CLSC tasarım problemini formüle etmek için yeni bir çift-amaçlı karma tamsayılı doğrusal programlama (KTDP) modelini kullanmaktadır. Modelin iki yönlülüğünü sağlamak adına hedefe ulaşma yöntemi (GAM) kullanılmış ve daha sonra Gurobi Python API kullanılararak önerilen modelin üç farklı ölçekteki (küçük, orta ve büyük) problemler üzerinde uygulanabilirliği test edilmiştir. Önerilen metodolojinin farklı problemler için en uygun çözümleri maksimum 500 saniyede bulabildiği gösterilmiştir. Son olarak, amaç fonksiyonlarının davranışlarını değerlendirmek ve yönetimsel öngörüler ve karar destek çıkarımları sağlamak için anahtar parametreler üzerinde bir dizi duyarlılık analizi yapılmaktadır. onuçlar modelin talep parametresine yüksek oranda bağlı olduğunu göstermektedir. Öyle ki, talepteki bir artış toplam talepteki artışla ve aynı anda servis seviyesinde görülen aşağı yönlü trendle yakında ilişkilidir.

Keywords

• Kapalı-Döngü Tedarik Ağı
• Çok Amaçlı Model
• Karmaşık-Tamsayılı Model
• Hedefe Ulaşma Yöntemi
• Duyarlılık Analizi

References

1. Aghighi, A., Goli, A., Malmir, B., & Tirkolaee, E. B. (2021). The stochastic location-routing-inventory problem of perishable products with reneging and balking. Journal of Ambient Intelligence and Humanized Computing, 1-20.
2. Amin, S. H., & Zhang, G. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems with Applications, 39(8), 6782-6791.
3. Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165-4176.
4. Amin, S. H., & Zhang, G. (2013). A three-stage model for closed-loop supply chain configuration under uncertainty. International Journal of Production Research, 51(5), 1405-1425.
5. Chuang, C. H., Wang, C. X., & Zhao, Y. (2014). Closed-loop supply chain models for a high-tech product under alternative reverse channel and collection cost structures. International Journal of Production Economics, 156, 108-123.
6. Dekker, R., Fleischmann, M., Inderfurth, K., & van Wassenhove, L. N. (Eds.). (2013). Reverse logistics: quantitative models for closed-loop supply chains.
7. Devika, K., Jafarian, A., & Nourbakhsh, V. (2014). Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques. European Journal of Operational Research, 235(3), 594-615.
8. Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). A stochastic multi-objective model for a closed-loop supply chain with environmental considerations. Applied Soft Computing, 69, 232-249.

9. Fathollahi-Fard, A. M., Ahmadi, A., & 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.
10. Garg, K., Kannan, D., Diabat, A., & Jha, P. C. (2015). A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design. Journal of Cleaner Production, 100, 297-314.
11. Gaur, J., Amini, M., & Rao, A. K. (2017). Closed-loop supply chain configuration for new and reconditioned products: An integrated optimization model. Omega, 66, 212-223.
12. Gembicki, F., & Haimes, Y. (1975). Approach to performance and sensitivity multiobjective optimization: The goal attainment method. IEEE Transactions on Automatic control, 20(6), 769-771.
13. Giri, B. C., & Sharma, S. (2015). Optimizing a closed-loop supply chain with manufacturing defects and quality dependent return rate. Journal of Manufacturing Systems, 35, 92-111.
14. Goli, A., Tirkolaee, E. B., & Weber, G. W. (2020). A perishable product sustainable supply chain network design problem with lead time and customer satisfaction using a hybrid whale-genetic algorithm. In Logistics operations and management for recycling and reuse (pp. 99-124). Springer, Berlin, Heidelberg.
15. Golroudbary, S. R., & Zahraee, S. M. (2015). System dynamics model for optimizing the recycling and collection of waste material in a closed-loop supply chain. Simulation Modelling Practice and Theory, 53, 88-102.
16. Govindan, K., Jha, P. C., & Garg, K. (2016). Product recovery optimization in closed-loop supply chain to improve sustainability in manufacturing. International Journal of Production Research, 54(5), 1463-1486.
17. Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European journal of operational research, 240(3), 603-626.
18. Hajiaghaei-Keshteli, M., & Fard, A. M. F. (2019). Sustainable closed-loop supply chain network design with discount supposition. Neural computing and applications, 31(9), 5343-5377.
19. Kannan, G., Noorul Haq, A., & 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.
20. Kannan, G., Sasikumar, P., & Devika, K. (2010). A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling. Applied mathematical modelling, 34(3), 655-670.
21. Kenné, J. P., Dejax, P., & Gharbi, A. (2012). Production planning of a hybrid manufacturing–remanufacturing system under uncertainty within a closed-loop supply chain. International Journal of Production Economics, 135(1), 81-93.
22. Kumar, N.R. and Kumar R.M.S. (2013). Closed Loop Supply Chain Management and Reverse Logistics – A Literature Review, International Journal of Engineering Research and Technology, 6(4), pp. 455-468.
23. Lotfi, R., Mehrjerdi, Y. Z., Pishvaee, M. S., Sadeghieh, A., & Weber, G. W. (2021). A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk. Numerical Algebra, Control & Optimization, 11(2), 221.
24. Mohammed, F., Selim, S. Z., Hassan, A., & Syed, M. N. (2017). Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transportation Research Part D: Transport and Environment, 51, 146-172.
25. Mondal, A., & Roy, S. K. (2021). Multi-objective sustainable opened-and closed-loop supply chain under mixed uncertainty during COVID-19 pandemic situation. Computers & Industrial Engineering, 159, 107453.
26. Olugu, E. U., & Wong, K. Y. (2012). An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry. Expert Systems with Applications, 39(1), 375-384.
27. Özceylan, E., & Paksoy, T. (2013). A mixed integer programming model for a closed-loop supply-chain network. International Journal of Production Research, 51(3), 718-734.
28. Paksoy, T., Bektaş, T., & Özceylan, E. (2011). Operational and environmental performance measures in a multi-product closed-loop supply chain. Transportation Research Part E: Logistics and Transportation Review, 47(4), 532-546.
29. Panda, S., Modak, N. M., & Cárdenas-Barrón, L. E. (2017). Coordinating a socially responsible closed-loop supply chain with product recycling. International Journal of Production Economics, 188, 11-21.
30. Pazhani, S., Mendoza, A., Nambirajan, R., Narendran, T. T., Ganesh, K., & Olivares-Benitez, E. (2021). Multi-period multi-product closed loop supply chain network design: A relaxation approach. Computers & Industrial Engineering, 155, 107191.
31. Peng, H., Shen, N., Liao, H., Xue, H., & Wang, Q. (2020). Uncertainty factors, methods, and solutions of closed-loop supply chain—A review for current situation and future prospects. Journal of Cleaner Production, 254, 120032.
32. Pervin, M., Roy, S. K., & Weber, G. W. (2019). Multi-item deteriorating two-echelon inventory model with price-and stock-dependent demand: A trade-credit policy. Journal of Industrial & Management Optimization, 15(3), 1345.
33. Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683.
34. Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied mathematical modelling, 35(2), 637-649.
35. Pochampally, K. K., Gupta, S. M., & Govindan, K. (2009). Metrics for performance measurement of a reverse/closed-loop supply chain. International Journal of Business Performance and Supply Chain Modelling, 1(1), 8-32.
36. Ramezani, M., Kimiagari, A. M., & Karimi, B. (2014). Closed-loop supply chain network design: A financial approach. Applied Mathematical Modelling, 38(15-16), 4099-4119.
37. Ruimin, M. A., Lifei, Y. A. O., Maozhu, J. I. N., Peiyu, R. E. N., & Zhihan, L. V. (2016). Robust environmental closed-loop supply chain design under uncertainty. Chaos, Solitons & Fractals, 89, 195-202.
38. Soleimani, H., & Kannan, G. (2015). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling, 39(14), 3990-4012.
39. Soleimani, H., Govindan, K., Saghafi, H., & Jafari, H. (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers & industrial engineering, 109, 191-203.
40. Stindt, D., & Sahamie, R. (2014). Review of research on closed loop supply chain management in the process industry. Flexible Services and Manufacturing Journal, 26(1), 268-293.
41. Yang, G. F., Wang, Z. P., & Li, X. Q. (2009). The optimization of the closed-loop supply chain network. Transportation Research Part E: Logistics and Transportation Review, 45(1), 16-28.
42. Yildizbaşi, A., Çalik, A., Paksoy, T., Farahani, R. Z., & Weber, G. W. (2018). Multi-level optimization of an automotive closed-loop supply chain network with interactive fuzzy programming approaches. Technological and Economic Development of Economy, 24(3), 1004-1028.
43. Zhen, L., Huang, L., & Wang, W. (2019). Green and sustainable closed-loop supply chain network design under uncertainty. Journal of Cleaner Production, 227, 1195-1209.