A novel collaborative electric vehicle routing problem with multiple prioritized time windows and time-dependent hybrid recharging

Due to global warming and environmental deterioration, the development of electric vehicles (EVs) is seen as a key measure to promote energy saving and emission reduction in logistics industries. However, EVs' limited driving range and long recharging time make them unable to be widely applied in logistics distribution activities. Thus, this study establishes a model for a novel collaborative electric vehicle routing problem with multiple prioritized time windows and time-dependent hybrid recharging to address the above difficulties. The model quantifies and incorporates the priority of different time windows into costs to better satisfy customer demands while minimizing the total costs. A time-dependent hybrid recharging strategy that integrates three modes of instation fast, in-station normal, and en-route battery swapping with collaborative mobile battery swapping vans (BSVs) is presented to reduce the impact of recharging time on route decision-making. The possible queuing time due to the limited capacity of recharging stations is considered to simulate the practical application scenarios. To effectively solve the complex model of synchronous optimization of EV and BSV routes, an extended adaptive large neighborhood search algorithm with two-dimensional coding scheme is proposed. In the proposed algorithm, several removal and insertion operators are integrated to effectively evolve the solutions, and the variable neighborhood descent algorithm is combined with local search operators to further improve the algorithmic performance. Comparison experiments were conducted to verify the effectiveness of the proposed algorithm by comparing with the other baseline algorithms.