A Multi Objective Optimization Model for Multi-commodity Closed-loop Supply Chain Network Considering Disruption Risk

Document Type : Original Article

Authors

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 Department Mathematics, Noor branch, Islamic Azad University, Noor, Iran

Abstract

Recently, the difference in the most effective competencies is considered the main competitive factor in organizations. To this end, organizations seek to improve a number of their functional capabilities, expertise, and capacities to enhance their operational area. Therefore, when an organization focuses on the quality of its services or products, it is trying to improve maintainability to gain a competitive advantage. In this study, a closed-loop, multi-objective, multi-level, multi-commodity, and multi-period mathematical model for a supply chain with producer, and distributor components is presented to locate and allocate items. The presented model can control environmental, economic, and social factors along the chain. One of the most important and unique aspects of the current study is considering different scenarios in the closed-loop supply chain (CLSC) so that the quality of the produced and transported products is paid attention to according to perishability. In addition, to control environmental effects, the model can minimize total CO2 emissions. The problem is solved on small, medium, and large scales using Epsilon Constraint and NSGA-II methods. According to the obtained results, the flow according to the boom scenario is more than the stagnation scenario. Finally, according to the sensitivity analysis, the number of centers established increases with an increase in demand. The results show that the non-dominated sorting genetic algorithm (NSGA-II) model can predict the behavior of the model well in the long term. For this purpose, Mean ideal distance (MID) index, has been used for evaluation of calculation. the value of standard MID is equal to 6.56 that shows the model accuracy is adequate.

Graphical Abstract

A Multi Objective Optimization Model for Multi-commodity Closed-loop Supply Chain Network Considering Disruption Risk

Keywords

References

  1. Ghasempoor Anaraki M, Vladislav DS, Karbasian M, Osintsev N, Nozick V. Evaluation and selection of supplier in supply chain with fuzzy analytical network process approach. Journal of fuzzy extension and applications. 2021;2(1):69-88. 10.22105/jfea.2021.274734.1078
  2. Yun Y, Chuluunsukh A, Gen M. Sustainable closed-loop supply chain design problem: A hybrid genetic algorithm approach. Mathematics. 2020;8(1):84. 10.3390/math8010084
  3. Vidergar P, Perc M, Lukman RK. A survey of the life cycle assessment of food supply chains. Journal of cleaner production. 2021;286:125506. https://doi.org/10.1016/j.jclepro.2020.125506
  4. Haghshenas P, Sahraeian R, Golmohammadi A-M. A state-of-the-art model of location, inventory, and pricing problem in the closed-loop supply chain network. International Journal of Engineering, Transactions B: Applications. 2022;35(8):1558-70. 10.5829/ije.2022.35.08B.12
  5. Wang Z, Wang Y, Li B, Cheng Y. Responsibility sharing strategy of product ecological design and collection in manufacturer-retailer closed-loop supply chain. Computers & Industrial Engineering. 2023;176:108926. https://doi.org/10.1016/j.cie.2022.108926
  6. Abolghasemian M, Kanafi AG, Daneshmand-Mehr M. Simulation-based multiobjective optimization of open-pit mine haulage system: a modified-NBI method and meta modeling approach. Complexity. 2022;2022. https://doi.org/10.1155/2022/3540736
  7. Chkanikova O, Sroufe R. Third-party sustainability certifications in food retailing: Certification design from a sustainable supply chain management perspective. Journal of Cleaner Production. 2021;282:124344. https://doi.org/10.1016/j.jclepro.2020.124344
  8. Ghasemi P, Hemmaty H, Pourghader Chobar A, Heidari MR, Keramati M. 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. 2023;10(3):412-26. 10.22105/jarie.2022.321454.1414
  9. Ostad-Ali-Askari K. Management of risks substances and sustainable development. Applied Water Science. 2022;12(4):65. 10.1007/s13201-021-01562-7
  10. Dai J, Xie L, Chu Z. Developing sustainable supply chain management: The interplay of institutional pressures and sustainability capabilities. Sustainable Production and Consumption. 2021;28:254-68. https://doi.org/10.1016/j.spc.2021.04.017
  11. Jahangiri S, Abolghasemian M, Ghasemi P, Chobar AP. Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions. International journal of industrial and systems engineering. 2023;43(1):1-19. https://doi.org/10.1504/IJISE.2023.128399
  12. Moosavi S, Seifbarghy M. A robust multi-objective fuzzy model for a green closed-loop supply chain network under uncertain demand and reliability (a case study in engine oil industry). International Journal of Engineering, Transactions C: Aspects 2021;34(12):2585-603. 10.5829/ije.2021.34.12c.03
  13. Diabat A, Jebali A. Multi-product and multi-period closed loop supply chain network design under take-back legislation. International Journal of Production Economics. 2021;231:107879. https://doi.org/10.1016/j.ijpe.2020.107879
  14. Pourghader Chobar A, Adibi MA, Kazemi A. A novel multi-objective model for hub location problem considering dynamic demand and environmental issues. Journal of industrial engineering and management studies. 2021;8(1):1-31. https://doi.org/10.22116/jiems.2021.239719.1373
  15. Ramesh A, Ostad‑Ali‑Askari K. Effect of effluent and magnetized effluent on Manning roughness coefficient in furrow irrigation. Applied Water Science. 2023;13(1):21. 10.1007/s13201-022-01818-w
  16. Emenike SN, Falcone G. A review on energy supply chain resilience through optimization. Renewable and Sustainable Energy Reviews. 2020;134:110088. 10.1016/j.rser.2020.110088
  17. Pourghader Chobar A, Sabk Ara M, Moradi Pirbalouti S, Khadem M, Bahrami S. 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. 2022;9(3):354-73. https://doi.org/10.22105/jarie.2021.299798.1365
  18. Sajedi S, Sarfaraz A, Bamdad S, Khalili-Damghani K. Designing a sustainable reverse logistics network considering the conditional value at risk and uncertainty of demand under different quality and market scenarios. International Journal of Engineering, Transactions B: Applications 2020;33(11):2252-71. 10.5829/ije.2020.33.11b.17
  19. Ullah M. Impact of transportation and carbon emissions on reverse channel selection in closed-loop supply chain management. Journal of Cleaner Production. 2023;394:136370. https://doi.org/10.1016/j.jclepro.2023.136370
  20. Fu R, Qiang QP, Ke K, Huang Z. Closed-loop supply chain network with interaction of forward and reverse logistics. Sustainable Production and Consumption. 2021;27:737-52. https://doi.org/10.1016/j.spc.2021.01.037
  21. Afshar M, Hadji Molana SM, Rahmani Parchicolaie B. Developing multi-objective mathematical model of sustainable multi-commodity, multi-level closed-loop supply chain network considering disruption risk under uncertainty. Journal of Industrial and Systems Engineering. 2022;14(3):280-302. 20.1001.1.17358272.2022.14.3.13.2
  22. Heydari Kushalshah T, Daneshmand-Mehr M, Abolghasemian M. Hybrid modelling for urban water supply system management based on a bi-objective mathematical model and system dynamics: A case study in Guilan province. Journal of Industrial and Systems Engineering. 2023;15(1):260-79.
  23. Ghasemi P, Abolghasemian M. A Stackelberg game for closed-loop supply chains under uncertainty with genetic algorithm and gray wolf optimization. Supply Chain Analytics. 2023;4:100040. https://doi.org/10.1016/j.sca.2023.100040
  24. Negri M, Cagno E, Colicchia C, Sarkis J. Integrating sustainability and resilience in the supply chain: A systematic literature review and a research agenda. Business Strategy and the environment. 2021;30(7):2858-86. https://doi.org/10.1002/bse.2776
  25. Chobar AP, Adibi MA, Kazemi A. Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment, Development and Sustainability. 2022:1-28. 10.1007/s10668-022-02350-2
  26. Zavala-Alcívar A, Verdecho M-J, Alfaro-Saiz J-J, editors. Assessing and selecting sustainable and resilient suppliers in agri-food supply chains using artificial intelligence: a short review. Boosting Collaborative Networks 40: 21st IFIP WG 55 Working Conference on Virtual Enterprises, PRO-VE 2020, Valencia, Spain, November 23–25, 2020, Proceedings 21; 2020: Springer. 10.1007/978-3-030-62412-5_41
  27. Rezaei Kallaj M, Abolghasemian M, Moradi Pirbalouti S, Sabk Ara M, Pourghader Chobar A. Vehicle routing problem in relief supply under a crisis condition considering blood types. Mathematical Problems in Engineering. 2021;2021:1-10. 10.1155/2021/7217182
  28. Özkan EY. Inequalities for approximation of new defined fuzzy post-quantum Bernstein polynomials via interval-valued fuzzy numbers. Symmetry. 2022;14(4):696. https://doi.org/10.3390/sym14040696
  29. Ozkan EY. Approximation by Fuzzy $(p, q) $-Bernstein-Chlodowsky Operators. Sahand Communications in Mathematical Analysis. 2022;19(2):113-32. https://doi.org/10.22130/scma.2022.524506.910
  30. Özkan EY, Hazarika B. Approximation results by fuzzy Bernstein type rational functions via interval-valued fuzzy number. Soft Computing. 2023;27(11):6893-901. 10.1007/s00500-023-08013-2
  31. Pasandideh SHR, Niaki STA, Asadi K. Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Information Sciences. 2015;292:57-74. https://doi.org/10.1016/j.ins.2014.08.068
  32. Ghomi-Avili M, Naeini SGJ, Tavakkoli-Moghaddam R, Jabbarzadeh A. A fuzzy pricing model for a green competitive closed-loop supply chain network design in the presence of disruptions. Journal of Cleaner Production. 2018;188:425-42. 10.1016/j.jclepro.2018.03.273
  33. Pishvaee MS, Razmi J, Torabi SA. An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain. Transportation Research Part E: Logistics and Transportation Review. 2014;67:14-38. https://doi.org/10.1016/j.tre.2014.04.001
  34. Khalifehzadeh S, Seifbarghy M, Naderi B. A four-echelon supply chain network design with shortage: Mathematical modeling and solution methods. Journal of Manufacturing Systems. 2015;35:164-75. https://doi.org/10.1016/j.jmsy.2014.12.002
  35. Zhalechian M, Tavakkoli-Moghaddam R, Zahiri B, Mohammadi M. Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation research part E: logistics and transportation review. 2016;89:182-214. https://doi.org/10.1016/j.tre.2016.02.011
  36. Rahmani D, Mahoodian V. Strategic and operational supply chain network design to reduce carbon emission considering reliability and robustness. Journal of Cleaner Production. 2017;149:607-20. https://doi.org/10.1016/j.jclepro.2017.02.068
  37. Nasr AK, Tavana M, Alavi B, Mina H. A novel fuzzy multi-objective circular supplier selection and order allocation model for sustainable closed-loop supply chains. Journal of Cleaner production. 2021;287:124994.
  38. Dong J, Jiang L, Lu W, Guo Q. Closed-loop supply chain models with product remanufacturing under random demand. Optimization. 2021;70(1):27-53. https://doi.org/10.1016/j.jclepro.2020.124994
  39. Wu C-H. A dynamic perspective of government intervention in a competitive closed-loop supply chain. European Journal of Operational Research. 2021;294(1):122-37. https://doi.org/10.1016/j.ejor.2021.01.014
  40. Moradi S, Sangari MS. A robust optimisation approach for designing a multi-echelon, multi-product, multi-period supply chain network with outsourcing. International Journal of Logistics Systems and Management. 2021;38(4):488-505. https://doi.org/10.1504/IJLSM.2021.114759
  41. Kalantari S, Kazemipoor H, Sobhani FM, Molana SMH. A neutrosophical model for optimal sustainable closed-loop supply chain network with considering inflation and carbon emission policies. Decision Making: Applications in Management and Engineering. 2022;5(2):46-77. https://doi.org/10.31181/dmame03051020224k
  42. Salehi-Amiri A, Zahedi A, Gholian-Jouybari F, Calvo EZR, Hajiaghaei-Keshteli M. Designing a closed-loop supply chain network considering social factors; a case study on avocado industry. Applied Mathematical Modelling. 2022;101:600-31. 10.1016/j.apm.2021.08.035
  43. Kalantari S, Kazemipoor H, Sobhani FM, Molana SMH. Designing sustainable closed-loop supply chain network with considering spot-to-point inflation and carbon emission policies: A case study. Computers & Industrial Engineering. 2022;174:108748. https://doi.org/10.1016/j.cie.2022.108748
  44. Garai A, Sarkar B. Economically independent reverse logistics of customer-centric closed-loop supply chain for herbal medicines and biofuel. Journal of Cleaner Production. 2022;334:129977. https://doi.org/10.1016/j.jclepro.2021.129977
  45. Abolghasemian M, Darabi H. Simulation based optimization of haulage system of an open-pit mine: Meta modeling approach. Organizational resources management researchs. 2018;8(2):1-17. 20.1001.1.22286977.1397.8.2.2.6
  46. Abolghasemian M, Ghane Kanafi A, Daneshmandmehr M. A two-phase simulation-based optimization of hauling system in open-pit mine. Iranian journal of management studies. 2020;13(4):705-32. 0.22059/IJMS.2020.294809.673898
  47. Devika K, Jafarian A, Nourbakhsh V. 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. 2014;235(3):594-615. https://doi.org/10.1016/j.ejor.2013.12.032
  48. Fathollahi-Fard AM, Dulebenets MA, Hajiaghaei–Keshteli M, Tavakkoli-Moghaddam R, Safaeian M, Mirzahosseinian H. Two hybrid meta-heuristic algorithms for a dual-channel closed-loop supply chain network design problem in the tire industry under uncertainty. Advanced engineering informatics. 2021;50:101418. https://doi.org/10.1016/j.aei.2021.101418
  49. Ali SM, Fathollahi-Fard AM, Ahnaf R, Wong KY. A multi-objective closed-loop supply chain under uncertainty: An efficient Lagrangian relaxation reformulation using a neighborhood-based algorithm. Journal of Cleaner Production. 2023;423:138702. https://doi.org/10.1016/j.jclepro.2023.138702
  50. Li B, Liu K, Chen Q, Lau Y-y, Dulebenets MA. A Necessity-Based Optimization Approach for Closed-Loop Logistics Considering Carbon Emission Penalties and Rewards under Uncertainty. Mathematics. 2023;11(21):4516. https://doi.org/10.3390/math11214516
  51. Asghari M, Afshari H, Mirzapour Al-e-hashem S, Fathollahi-Fard AM, Dulebenets MA. Pricing and advertising decisions in a direct-sales closed-loop supply chain. Computers & Industrial Engineering. 2022;171:108439. https://doi.org/10.1016/j.cie.2022.108439
  52. Moosavi J, Fathollahi-Fard AM, Dulebenets MA. Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies. International Journal of Disaster Risk Reduction. 2022;75:102983. https://doi.org/10.1016/j.ijdrr.2022.102983
International Journal of Engineering
Volume 37, Issue 4
TRANSACTIONS A: Basics
April 2024
Pages 646-661

Files

Share

How to cite

Statistics