An Improved Bilevel Algorithm Based on Ant Colony Optimization and Adaptive Large Neighborhood Search for Routing and Charging Scheduling of Electric Vehicles

The past few decades have witnessed the boom of electric vehicles (EVs) techniques in response to their energy efficiency and reduction of the carbon footprint. However, limited cruising range and sparse charging infrastructure could restrain a massive deployment of EVs. To mitigate the problem, the need for routing and charging scheduling algorithms subject to minimization of time and economic costs emerged. The objective of this article is to propose an improved ant colony optimization (ACO) and adaptive large neighborhood search (ALNS)-based bilevel algorithm for the solvability of routing and charging scheduling problem of EVs. Specifically, in the first stage, the feasibility of EVs’ journeys is enhanced through two procedures: ameliorating the computation method for individual ants’ node selection probabilities and upgrading ACO’s pheromone update strategy after each iteration. In the second stage, the initial solutions from the final solutions of latter procedure are updated using different destroy and repair operators to optimize heuristic solutions. Finally, the effectiveness and superiority of the proposed algorithm are evaluated by comparisons with two other heuristic algorithms, and it is shown that the proposed algorithm provides better solution performance in terms of less time and economic costs based on the road network model of the Suzhou-Wuxi Highway Network.