A novel location-routing problem in electric vehicle transportation with stochastic demands

With the development of the electric vehicle (EV) technology, the electric vehicle routing problems (EVRPs) have become a current focus for research. Although customer demands in the logistics service industry are often uncertain during the route planning stage, these demands have seldom been discussed in the existing literature on the EVRPs. This study presents an electric vehicle battery swap station (BSS) location-routing problem with stochastic demands, with the aim to determine a minimum cost scheme including the optimal number and location of BSSs with an optimal route plan based on stochastic customer demands. Furthermore, the classical recourse policy and preventive restocking policy are extended by considering the influences of both battery and vehicle capacity simultaneously. Subsequently, the concept of Pareto optimality is applied to the EVRP to expedite the selection of BSS sequences. To solve such a hybrid problem, a hybrid variable neighborhood search (HVNS) algorithm is proposed, which integrates the binary particle swarm optimization and variable neighborhood search to solve the location and routing problems interactively. In experimental studies, the HVNS is compared to five heuristic algorithms to verify its performance.