Fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations

Abstract As fuel prices increase and emission regulations become increasingly strict, electric vehicles have been used in various logistics distribution activities. Most studies have focused on the electric vehicle routing problem under a deterministic environment, neglecting the effects of uncertain factors in practical logistics distribution. Thus, a novel fuzzy electric vehicle routing problem with time windows and recharging stations (FEVRPTW) is investigated in this study, and a fuzzy optimization model is established based on credibility theory for this problem. In the presented model, fuzzy numbers are used to denote the uncertainties of service time, battery energy consumption, and travel time. Moreover, the partial recharge is allowed under the uncertain environment. To solve the model, an adaptive large neighborhood search (ALNS) algorithm enhanced with the fuzzy simulation method is proposed. In the proposed ALNS algorithm, four new removal algorithms are designed and integrated for addressing the FEVRPTW. To further improve the algorithmic performance, the variable neighborhood descent algorithm is embedded into the proposed ALNS algorithm and five local search operators are applied. The experiments were conducted to verify the effectiveness of the proposed ALNS algorithm for solving the presented model.