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Closed-loop supply chain inventory management with recovery information of reusable containers

Author

Listed:
  • Tianji Yang

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education)

  • Chao Fu

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education)

  • Xinbao Liu

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education)

  • Jun Pei

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education)

  • Lin Liu

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education)

  • Panos M. Pardalos

    (University of Florida)

Abstract

This paper considers a closed-loop supply chain consisting of one-manufacturer and one-retailer. This supply chain provides single-kind products with reusable containers. The main purpose of this study is to explore and evaluate the value of recovery information captured by embedded sensors in the environment of internet of things. The recovery information of containers dynamically monitors recovery status and provides a reliable estimation of return quantity. The value of information is measured by the cost saving performances with full, partial or no recovery information. When the full or partial recovery information is available, the decisions are made based on the known quantities of the usable or total return flow. When no recovery information is available, the decisions are made based on the stationary distribution of the return flow. A periodic inventory model is built with uncertainties of forward and reverse flows. Then, a myopic order policy is proposed for the different levels of information utilization. Through the optimality analysis, we introduce a farsighted inventory control policy. Using the general result of Markov decision processes, the performance of heuristic policies is displayed. The farsighted policy performs better than the myopic policy. In addition, the farsighted policy helps to lessen the convex impact of utilization rate on the expected cost. Afterwards, we extend the model with the selective disposal behavior. A simulation study is used to depict sensitivity and robustness of the farsighted policy. Finally, we extend the simulation experiment with uniformly distributed in-use time for a more general applicability.

Suggested Citation

  • Tianji Yang & Chao Fu & Xinbao Liu & Jun Pei & Lin Liu & Panos M. Pardalos, 2018. "Closed-loop supply chain inventory management with recovery information of reusable containers," Journal of Combinatorial Optimization, Springer, vol. 35(1), pages 266-292, January.
    • Handle: RePEc:spr:jcomop:v:35:y:2018:i:1:d:10.1007_s10878-015-9987-2
    DOI: 10.1007/s10878-015-9987-2
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    References listed on IDEAS

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    Cited by:

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    2. Tan, Weng Chun & Sidhu, Manjit Singh, 2022. "Review of RFID and IoT integration in supply chain management," Operations Research Perspectives, Elsevier, vol. 9(C).
    3. Mahmoudi, Monirehalsadat & Parviziomran, Irandokht, 2020. "Reusable packaging in supply chains: A review of environmental and economic impacts, logistics system designs, and operations management," International Journal of Production Economics, Elsevier, vol. 228(C).
    4. Pablo Becerra & Josefa Mula & Raquel Sanchis, 2022. "Sustainable Inventory Management in Supply Chains: Trends and Further Research," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
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    7. Man Yang & Tao Zhang, 2023. "Demand forecasting and information sharing of a green supply chain considering data company," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-28, July.

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