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Green vehicle routing optimization based on dynamic constraint selection co-evolutionary algorithm.

Lu-Jie Zhou1, Hai-Fei Zhang2, Jun-Hao Fu3,4

  • 1School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin, 300222, China.

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|May 21, 2025
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Summary
This summary is machine-generated.

This study introduces a new model for green vehicle routing, considering real-time traffic and delivery time windows. The developed algorithm effectively reduces costs and emissions for logistics operations.

Keywords:
Co-evolutionary algorithmDynamic constraint selectionLogistics distributionMulti-objective optimizationTime-varying property

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Area of Science:

  • Operations Research
  • Transportation Logistics
  • Environmental Sustainability

Background:

  • Existing green vehicle routing models often lack single-objective solutions and fail to account for real-time speed variations.
  • Dynamic factors like changing speeds and capacities significantly impact carbon emissions in logistics.

Purpose of the Study:

  • To establish a multi-objective green vehicle routing problem with time window constraints in time-varying conditions (MOGVRPTW-TV).
  • To propose a co-evolutionary framework-based constrained multi-objective evolutionary algorithm for solving the MOGVRPTW-TV.

Main Methods:

  • Developed a model incorporating time-varying speed and capacity variations affecting carbon emissions.
  • Proposed a co-evolutionary algorithm featuring shift crowding distance for solution diversity and a dynamic constraint selection strategy for optimization.

Main Results:

  • The algorithm effectively avoids traffic congestion periods.
  • Demonstrated significant reductions in overall distribution costs.
  • Achieved lower fuel consumption and carbon emissions for vehicles.

Conclusions:

  • The proposed MOGVRPTW-TV model and its associated algorithm offer an effective solution for complex logistics challenges.
  • The approach balances convergence and diversity, leading to improved efficiency and environmental benefits in green logistics.