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Closed loop supply chain networks: Designs for energy and time value efficiency

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  • Kadambala, Dinesh K.
  • Subramanian, Nachiappan
  • Tiwari, Manoj K.
  • Abdulrahman, Muhammad
  • Liu, Chang

Abstract

Product recovery has become a viable option for many industries to realize economic gains while protecting the environment. However, insufficient investment and inefficient supply chains have hampered the viability of reuse and/or recycling because of the extended time intervals between the recycling process of recovery and reuse. Manufacturers and distributors face the challenge and necessity to reduce these process delays in order to recover the maximum value of the returned products through an effective, responsive closed loop supply chain (CLSC). This paper quantitatively measures the effective responsiveness of the CLSC model in terms of time and energy efficiency. The proposed multi-objective mixed integer linear programming (MOMILP) model evaluates delay parameters with decision variables that maximize profit, optimize customer surplus and minimize energy use. The model suggests decision makers may achieve an optimal tradeoff among differing objectives in a multiple-objective CLSC scenario. We employed a multi-objective particle swarm optimization (MOPSO) approach to solve the proposed MOMILP model and compared our approach with the Non-Dominated Sorted Genetic Algorithm (NSGA-II) for optimal solution. Results of the comparative evolutionary approaches shows that MOPSO outperforms NSGA-II in almost all cases in achieving the best trade-off solutions. Sensitivity analysis carried out to test the robustness of the model confirms that substantially less cost is feasible through the reduction of return process delays. This paper aims to formulate a multi-objective CLSC problem based on a network-flow model measuring the time value to recover maximum assets lost due to delay at different stages of the recycle process. We also developed a particle swarm approach for a multi-objective CLSC. Our study also offers valuable insights for designers wishing to create a product flow network with an optimal capacity level in case of prioritized objectives scenarios.

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  • Kadambala, Dinesh K. & Subramanian, Nachiappan & Tiwari, Manoj K. & Abdulrahman, Muhammad & Liu, Chang, 2017. "Closed loop supply chain networks: Designs for energy and time value efficiency," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 382-393.
    • Handle: RePEc:eee:proeco:v:183:y:2017:i:pb:p:382-393
    DOI: 10.1016/j.ijpe.2016.02.004
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    1. Sung Hoon Chung & Robert D. Weaver & Hyun Woo Jeon, 2020. "Sustainable Management of Remanufacturing in Dynamic Supply Chains," Networks and Spatial Economics, Springer, vol. 20(3), pages 703-731, September.
    2. Sadjady Naeeni, Hannan & Sabbaghi, Navid, 2022. "Sustainable supply chain network design: A case of the glass manufacturer in Asia," International Journal of Production Economics, Elsevier, vol. 248(C).
    3. Majid Eskandarpour & Pierre Dejax & Olivier Péton, 2019. "Multi-Directional Local Search for Sustainable Supply Chain Network Design," Post-Print hal-02407741, HAL.
    4. Joakim Haraldsson & Maria T. Johansson, 2019. "Energy Efficiency in the Supply Chains of the Aluminium Industry: The Cases of Five Products Made in Sweden," Energies, MDPI, vol. 12(2), pages 1-25, January.
    5. Letmathe, Peter & Wagner, Sandra, 2018. "“Messy” marginal costs: Internal pricing of environmental aspects on the firm level," International Journal of Production Economics, Elsevier, vol. 201(C), pages 41-52.
    6. Xiaodong Lv & Angfei Li & Shuhong Wang & Tao Zhang, 2023. "Building a demand-oriented optimal model for the recycling of used electronic products," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 5949-5971, July.
    7. Qiao, Haike & Su, Qin, 2021. "Distribution channel and licensing strategy choice considering consumer online reviews in a closed-loop supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    8. Chamari Pamoshika Jayarathna & Duzgun Agdas & Les Dawes & Tan Yigitcanlar, 2021. "Multi-Objective Optimization for Sustainable Supply Chain and Logistics: A Review," Sustainability, MDPI, vol. 13(24), pages 1-31, December.
    9. Chen, Wenyi & Kucukyazici, Beste & Saenz, Maria Jesus, 2019. "On the joint dynamics of the economic and environmental performances for collective take-back systems," International Journal of Production Economics, Elsevier, vol. 218(C), pages 228-244.
    10. Manju Saroha & Dixit Garg & Sunil Luthra, 2022. "Analyzing the circular supply chain management performance measurement framework: the modified balanced scorecard technique," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 951-960, June.
    11. Muyldermans, L. & Van Wassenhove, L.N. & Guide, V.D.R., 2019. "Managing high-end ex-demonstration product returns," European Journal of Operational Research, Elsevier, vol. 277(1), pages 195-214.
    12. Beatrice Marchi & Simone Zanoni, 2017. "Supply Chain Management for Improved Energy Efficiency: Review and Opportunities," Energies, MDPI, vol. 10(10), pages 1-29, October.
    13. Huang, Yanting & Wang, Zongjun, 2017. "Information sharing in a closed-loop supply chain with technology licensing," International Journal of Production Economics, Elsevier, vol. 191(C), pages 113-127.
    14. Wang, Yihan & Chen, Chen & Tao, Yuan & Wen, Zongguo & Chen, Bin & Zhang, Hong, 2019. "A many-objective optimization of industrial environmental management using NSGA-III: A case of China’s iron and steel industry," Applied Energy, Elsevier, vol. 242(C), pages 46-56.
    15. Luttiely Santos Oliveira & Ricardo Luiz Machado, 2021. "Application of optimization methods in the closed-loop supply chain: a literature review," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 357-400, February.
    16. Xiaobao Zhu & Jing Shi & Fengjie Xie & Rouqi Song, 2020. "Pricing strategy and system performance in a cloud-based manufacturing system built on blockchain technology," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1985-2002, December.

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