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Inventory sharing strategy and optimization for reusable transport items

Author

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  • Liu, Guoquan
  • Li, Lei
  • Chen, Jianghang
  • Ma, Fei

Abstract

Reusable transport items (RTI), as a sustainable solution to the increasing packaging wastes generated along with the ever-growing globalized supply chains, create both challenges and opportunities to organizations for their management. A trend of organizations outsourcing their RTI activities explains the rapid emerging of a third-party RTI pooler. Yet few research studies have been conducted from the perspective of the RTI pooler for optimizing their customer allocation, and few on the decision support model for a practical case in the developing economies. This research intends to fill in this gap. Motivated by a case study of a leading RTI pooling company in China, this research proposes to implement a sharing strategy into the daily planning operation of distribution and routing. A decision support framework is developed to optimize the distribution flows and dispatching vehicle routes by the use of a two-stage solution process. Empirical results demonstrate an economic savings of 28.1% in the transportation costs along with environmental and social advantages implicated by the shortened travel distance of vehicles.

Suggested Citation

  • Liu, Guoquan & Li, Lei & Chen, Jianghang & Ma, Fei, 2020. "Inventory sharing strategy and optimization for reusable transport items," International Journal of Production Economics, Elsevier, vol. 228(C).
  • Handle: RePEc:eee:proeco:v:228:y:2020:i:c:s0925527320301286
    DOI: 10.1016/j.ijpe.2020.107742
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    References listed on IDEAS

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    Cited by:

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    2. Guo, Dongliang & Fan, Zhi-Ping & Sun, Minghe, 2022. "B2C car-sharing services: Sharing mode selection and value-added service investment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    3. Fatima Ezzahra Achamrah & Fouad Riane & Evren Sahin & Sabine Limbourg, 2022. "An Artificial-Immune-System-Based Algorithm Enhanced with Deep Reinforcement Learning for Solving Returnable Transport Item Problems," Sustainability, MDPI, vol. 14(10), pages 1-29, May.
    4. Yanqi Zhang & Xiaofei Kou & Haibin Liu & Shiqing Zhang & Liangliang Qie, 2022. "IoT-Enabled Sustainable and Cost-Efficient Returnable Transport Management Strategies in Multimodal Transport Systems," Sustainability, MDPI, vol. 14(18), pages 1-22, September.
    5. Jarosław Ziółkowski & Aleksandra Lęgas & Elżbieta Szymczyk & Jerzy Małachowski & Mateusz Oszczypała & Joanna Szkutnik-Rogoż, 2022. "Optimization of the Delivery Time within the Distribution Network, Taking into Account Fuel Consumption and the Level of Carbon Dioxide Emissions into the Atmosphere," Energies, MDPI, vol. 15(14), pages 1-22, July.

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