An Adaptive Memetic Algorithm for Dynamic Electric Vehicle Routing Problem with Time-Varying Demands

Dynamic electric vehicle routing problem (DEVRP) is an extension of the electric vehicle routing problem (EVRP) into dynamic logistical transportation system such that the demand of customer may change over time. The routing decision of DEVRP must concern with the driving range limitation of electric vehicle (EV) in a dynamic environment since both load degree and battery capacity are variable according to the time-varying demands. This paper proposes an adaptive memetic algorithm, where a special encoding strategy, an adaptive local search operator, and an economical random immigrant scheme are employed in the framework of evolutionary algorithm, to solve DEVRP efficiently. Numeric experiments are carried out upon a series of test instances that are constructed from a stationary VRP benchmark. The computational results show that the proposed algorithm is more effective in finding high-quality solution than several peer algorithms as well as significant in improving the capacity of the routing plan of EVs in dynamic transportation environment.