Multi-objective optimization of electric vehicle routing problem with battery swap and mixed time windows

With the growing interest in green logistics, the electric vehicles have been widely used as an important distribution means. In this paper, the electric vehicle routing problem with battery swap consideration and mixed time windows constraints (EVRP-BS-MTW) is proposed. The problem aims to minimize the total distribution costs and maximize the average utilization of batteries simultaneously, meeting both the environmental and economic interests. To solve this problem, a multi-objective whale optimization algorithm enhanced by particle filter and Levy flights (MWOA-PFLF) is developed. The introduction of particle filter makes it possible to predict the near optimal solutions at each iteration, meanwhile, the combination of Levy flights contributes to escape from local optimum and accelerate convergence. Experimental results have verified the efficiency of the neighborhood search strategies. The results also indicate that the proposed MWOA-PFLF outperforms the comparison algorithms both in solution quality and convergence rate.

Automated summary

This paper proposes a multi-objective optimization algorithm to solve the electric vehicle routing problem with battery swap consideration and time windows constraints. Experimental results show that this algorithm outperforms other algorithms in terms of solution quality and convergence rate.