Abstract
In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.
Highlights
As cleaner alternatives to fossil fuel-based vehicles (FFVs), electric freight vehicles (EFVs) have been widely used in logistics and transportation to deal with environmental challenges
It is of great practical significance to study and analyze logistics transportation based on electric vehicles
This paper extends the classical vehicle routing problems (VRPs) to the battery swapping electric vehicle routing problem with energy consumption and carbon emissions (BVRP-EC)
Summary
As cleaner alternatives to fossil fuel-based vehicles (FFVs), electric freight vehicles (EFVs) have been widely used in logistics and transportation to deal with environmental challenges. After taking the limited driving range and payload into account, how to plan the delivery routes and swap batteries to serve customers in a timely manner becomes an important research problem. We refer to this as the battery swapping electric vehicle routing problem with energy consumption and carbon emissions (BVRP-EC). BVRP-EC seeks to optimize the logistics operations in a cost-efficient way while reducing energy consumption and carbon emissions, which increases both economic and environmental performance. We study the BVRP-EC and build a corresponding mixed integer programming model This model differs from traditional VRPs by considering new constraints such as EFV driving range limitations and battery swapping strategies.
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