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Green Transportation Model in Logistics Considering the Carbon Emissions Costs Based on Improved Grey Wolf Algorithm

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  • Yiqin Lu

    (School of Economics and Management, Shanghai University of Electric Power, Shanghai 201306, China)

  • Shuang Li

    (School of Economics and Management, Shanghai University of Electric Power, Shanghai 201306, China)

Abstract

The use of new energy vehicles in transportation can effectively promote the development of green logistics. This study selects heavy–duty diesel trucks as traditional logistics vehicles and heavy–duty electric trucks as new energy logistics vehicles. A green transportation model considering carbon emission costs is established to analyze whether new energy logistics vehicles should be used in long–distance freight delivery and how to arrange the use of two types of logistics vehicles. The model is solved using a grey wolf optimization algorithm, which incorporates good point sets, dynamic adaptive inertia weights, and memory–guided location update equations. The model is then applied to three logistics companies in Zhejiang province, China. In addition, considering the time constraints of the logistics industry, the model is used to simulate the arrangement of logistics transport companies for two types of vehicles in long–distance transportation of goods under realistic situations. Finally, this paper studies the future arrangements for long–distance transportation of goods by logistics companies considering the growing popularity of charging piles and advancements in production technology for new energy vehicles. The results show that the involvement of more new energy logistics vehicles in long–distance transport results in lower transportation costs and reduced pollution generated during transportation.

Suggested Citation

  • Yiqin Lu & Shuang Li, 2023. "Green Transportation Model in Logistics Considering the Carbon Emissions Costs Based on Improved Grey Wolf Algorithm," Sustainability, MDPI, vol. 15(14), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11090-:d:1195128
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    References listed on IDEAS

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