An improved optimization algorithm for a multi-depot vehicle routing problem considering carbon emissions.

Area of Science:

• Operations Research
• Environmental Science
• Logistics Management

Background:

• The multi-depot vehicle routing problem (MDVRP) significantly impacts urban logistics costs and carbon emissions.
• Driving distance and vehicle loading are key factors influencing fuel consumption and environmental impact.

Purpose of the Study:

• To minimize logistics costs and driven distance in same-city deliveries by incorporating fuel and carbon emission costs.
• To develop a methodological guide for addressing MDVRPs with dual objectives of cost reduction and emission mitigation.

Main Methods:

• Formulation of a mixed integer programming model for the MDVRP.
• Design of an improved chemical reaction optimization algorithm with a greedy initial population strategy.
• Application of two-part and matrix coding approaches with four chemical reaction operators.

Main Results:

• Simulation experiments confirm that reducing driving distance leads to decreased carbon emissions.
• The proposed optimization algorithm effectively addresses the complexities of the MDVRP with cost and emission considerations.

Conclusions:

• Integrating fuel and carbon emission costs into MDVRP models is crucial for sustainable urban logistics.
• The developed optimization approach provides a practical framework for reducing environmental impact in delivery operations.