A hybrid genetic algorithm for the electric vehicle routing problem with time windows

Driven by the new legislation on greenhouse gas emissions, carriers began to use electric vehicles (EVs) for logistics transportation. This paper addresses an electric vehicle routing problem with time windows (EVRPTW). The electricity consumption of EVs is expressed by the battery state-of-charge (SoC). To make it more realistic, we take into account the terrain grades of roads, which affect the travel process of EVs. Within our work, the battery SoC dynamics of EVs are used to describe this situation. We aim to minimize the total electricity consumption while serving a set of customers. To tackle this problem, we formulate the problem as a mixed integer programming model. Furthermore, we develop a hybrid genetic algorithm (GA) that combines the 2-opt algorithm with GA. In simulation results, by the comparison of the simulated annealing (SA) algorithm and GA, the proposed approach indicates that it can provide better solutions in a short time.