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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A multi-center joint distribution optimization model considering carbon emissions and customer satisfaction.

Xiangyang Ren1, Xinxin Jiang1, Liyuan Ren1

  • 1School of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China.

Mathematical Biosciences and Engineering : MBE
|January 18, 2023
PubMed
Summary

This study introduces a green logistics solution by optimizing the vehicle routing problem with time windows to balance economic and environmental factors. The improved ant colony algorithm effectively reduces costs and carbon emissions while enhancing customer satisfaction.

Keywords:
green logisticsimproved ant colony optimizationjoint distributionpath optimizationtime window

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Area of Science:

  • Operations Research
  • Environmental Science
  • Logistics Management

Background:

  • Logistics enterprises face pressure to adopt sustainable practices, balancing economic viability with environmental responsibility.
  • Green logistics development necessitates integrating environmental costs, such as carbon emissions, into operational models.
  • The vehicle routing problem with time windows (VRPTW) is a critical area for optimization in distribution networks.

Purpose of the Study:

  • To develop a multi-center joint distribution optimization model for green logistics.
  • To integrate carbon emissions, distribution costs, and customer satisfaction into a unified optimization framework.
  • To address the challenge of reducing environmental impact while maintaining economic efficiency in logistics operations.

Main Methods:

  • Formulated a multi-center joint distribution optimization model incorporating distribution cost, carbon emission, and customer satisfaction.
  • Selected vehicle load rate and vehicle distance as key indicators for carbon emission assessment.
  • Designed an improved ant colony algorithm with elite strategy, saving strategy, vehicle service rules, and customer selection rules to solve the model.

Main Results:

  • The improved ant colony algorithm demonstrated superior performance compared to traditional ant colony optimization and genetic algorithms.
  • Simulation results indicated a significant reduction in distribution costs.
  • The model effectively decreased carbon emissions and improved overall customer satisfaction.

Conclusions:

  • The proposed optimization model and improved ant colony algorithm offer a sustainable solution for green logistics development.
  • Balancing economic, environmental, and customer satisfaction factors is achievable through integrated optimization.
  • This approach provides a practical framework for logistics enterprises aiming for greener operations.