Ant Colony optimization for Electric Vehicle Routing Problem with Capacity and Charging Time Constraints

Electric Vehicle Routing Problem (EVRP) is considerably challenging due to the capacity and electricity constraints of electric vehicles (EVs). Most existing studies on EVRP consider no limits on charging times when optimizing the routes of EVs. However, due to the long time of charging, the charging times of EVs are usually limited due to the urgent service demands of customers. To simulate this practical problem, this paper first formulates the EVRP with both capacity and charging time constraints (EVRP-CC). To tackle this new optimization problem, this paper further devises a two-stage solution construction method for ant colony optimization (ACO) to build feasible solutions to EVRP-CC. Subsequently, we embed the proposed method into five popular and classical ACO algorithms, namely ant system (AS), ranking based ant system (Rank-AS), elite ant system (EAS), max-min ant system (MMAS), and ant colony system (ACS), to solve EVRP-CC. Extensive experiments conducted on several instances generated from the widely used EVRP benchmark set demonstrate that the proposed solution construction method is effective to help ACO to solve EVRP-CC. In particular, Rank-AS with the proposed solution construction method achieves the best overall performance in solving EVRP-CC.