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Resource Matching in the Supply Chain Based on Environmental Friendliness under a Smart Contract

Author

Listed:
  • Jinyu Wei

    (School of Management, Tianjin University of Technology, Tianjin 300384, China)

  • Zihan Liang

    (School of Management, Tianjin University of Technology, Tianjin 300384, China)

  • Yaoxi Liu

    (School of Management, Tianjin University of Technology, Tianjin 300384, China)

  • Xin Yang

    (School of Economics and Management, Tianjin Agricultural University, Tianjin 300384, China)

Abstract

This study aims to solve the problem of environmental pollution caused by industry through the upgrading and transformation of the supply chain, supply chain resource allocation, and related aspects. Specifically, environmental friendliness is added to the resource-matching problem of the cloud platform supply chain. Additionally, learning theory and dynamic evaluation systems are introduced when creating a preference sequence. The deferred-acceptance algorithm is used for matching. Finally, the automatic matching of blockchain smart contracts ensures the interests of both matching parties. Through the analysis of the example at the end of the study, we found that (1) the deviation table of demand side 5 and supply side 7 in the example shows that the deviation between demand side 5 as demand side and supply side 7 is only 11.55186, and the deviation between supply side 7 as demand side and demand side 5 is only 6.56778, and both sides form a high-quality pairing when matched with other partners. No excessive waste of its resources occurs. (2) Effectively ensure the openness and transparency of the supply chain production process; (3) The impact of environmental factors on enterprises is fully considered. In the analysis of the calculation cases, it can be found that demand side 10 has extremely high requirements for the environmental friendliness of its partners, and although supplier 2 has a very high preference for demand side 10, it is not successfully matched because the environmental friendliness of its own enterprise is not up to the standard, while supplier 1 has an environmental friendliness of up to 92 and is finally matched with Demand side 10; (4) Through the comparison test in the appendix, it can be found that the improved GS algorithm achieves the distinction between positive and negative partners. After multiple rounds of scoring, positive demand side 1, 3 was matched with positive supply side 2, 4, which can strengthen the enthusiasm of both partners and avoid negative cooperation.

Suggested Citation

  • Jinyu Wei & Zihan Liang & Yaoxi Liu & Xin Yang, 2023. "Resource Matching in the Supply Chain Based on Environmental Friendliness under a Smart Contract," Sustainability, MDPI, vol. 15(2), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1505-:d:1033894
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    References listed on IDEAS

    as
    1. Qin Yang & Jinfeng Liu & Xing Liu & Cejun Cao & Wei Zhang, 2019. "A Two-Sided Matching Model for Task Distribution in Ridesharing: A Sustainable Operations Perspective," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    2. Ehsan Aghamohammadzadeh & Omid Fatahi Valilai, 2020. "A novel cloud manufacturing service composition platform enabled by Blockchain technology," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5280-5298, September.
    3. Vicki Knoblauch, 2009. "Marriage matching and gender satisfaction," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 32(1), pages 15-27, January.
    4. T.C. Cheng & Guoqing Wang, 2000. "Single Machine Scheduling with Learning Effect Considerations," Annals of Operations Research, Springer, vol. 98(1), pages 273-290, December.
    5. Ru Liang & Changzhi Wu & Zhaohan Sheng & Xiangyu Wang, 2018. "Multi-Criterion Two-Sided Matching of Public–Private Partnership Infrastructure Projects: Criteria and Methods," Sustainability, MDPI, vol. 10(4), pages 1-22, April.
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