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Optimizing Freight Vehicle Routing in Dynamic Time-Varying Networks with Carbon Dioxide Emission Trajectory Analysis

Author

Listed:
  • Rui Song

    (School of Logistics and Transportation, Central South University of Forestry and Technology, Changsha 410004, China)

  • Wanen Qin

    (School of Logistics and Transportation, Central South University of Forestry and Technology, Changsha 410004, China)

  • Wen Shi

    (School of Logistics and Transportation, Central South University of Forestry and Technology, Changsha 410004, China)

  • Xingjian Xue

    (School of Landscape Architecture, Central South University of Forestry and Technology, Changsha 410004, China)

Abstract

In this study, we formulate a freight vehicle path-planning model in the context of dynamic time-varying networks that aims to capture the spatial and temporal distribution characteristics inherent in the carbon dioxide emission trajectories of freight vehicles. Central to this model is the minimization of total carbon dioxide emissions from vehicle distribution, based on the comprehensive modal emission model (CMEM). Our model also employs the freight vehicle travel time discretization technique and the dynamic time-varying multi-path selection strategy. We then design an improved genetic algorithm to solve this complicated problem. Empirical results vividly illustrate the superior performance of our model over alternative objective function models. In addition, our observations highlight the central role of accurate period partitioning in time segmentation considerations. Finally, the experimental results underline that our multi-path model is able to detect the imprint of holiday-related effects on the spatial and temporal distribution of carbon dioxide emission trajectories, especially when compared to traditional single-path models.

Suggested Citation

  • Rui Song & Wanen Qin & Wen Shi & Xingjian Xue, 2023. "Optimizing Freight Vehicle Routing in Dynamic Time-Varying Networks with Carbon Dioxide Emission Trajectory Analysis," Sustainability, MDPI, vol. 15(21), pages 1-24, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15504-:d:1271952
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    References listed on IDEAS

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