Dynamic electric vehicle routing problem considering mid-route recharging and new demand arrival using an improved memetic algorithm

With the widespread popularity of electric vehicles (EVs) in recent years, electric vehicle routing problem (EVRP) has received considerable attention from logistics management field. However, the real-world logistics and transportation system is always time-varying, such as new customer demand arrives dynamically. This dynamic transportation environment poses a great deal of challenges upon the decision-making of EVRP since the mid-route recharging of EVs has to be considered to meet the dynamic arrival of new customer demand due to the limited battery capacity. Therefore, a dynamic EVRP (DEVRP) with mid-route recharging and new demand arrival is investigated in this study. A mathematical model considering both recharging and routing decision-makings is developed, and then an improved memetic algorithm (IMA) with adaptive local search and economical random immigrant strategy is proposed. Four peer algorithms are used to test the performance of the proposed IMA on numerous extended benchmarks. It is observed that IMA can be treated as a more effective solution tool to achieve a better routing plan as well as the improved energy efficiency of EVs.