Efficient algorithms for electric vehicles' min-max routing problem
This work addresses routing and recharging challenges for electric vehicle fleets in logistics, offering incremental improvements for companies transitioning to sustainable operations.
The paper tackles the min-max electric vehicle routing problem (MEVRP) to minimize the maximum distance traveled by any EV while considering charging stations, proposing a branch and cut framework and a hybrid heuristic algorithm that efficiently solves various instances with extensive computational validation.
An increase in greenhouse gases emission from the transportation sector has led companies and the government to elevate and support the production of electric vehicles (EV). With recent developments in urbanization and e-commerce, transportation companies are replacing their conventional fleet with EVs to strengthen the efforts for sustainable and environment-friendly operations. However, deploying a fleet of EVs asks for efficient routing and recharging strategies to alleviate their limited range and mitigate the battery degradation rate. In this work, a fleet of electric vehicles is considered for transportation and logistic capabilities with limited battery capacity and scarce charging station availability. We introduce a min-max electric vehicle routing problem (MEVRP) where the maximum distance traveled by any EV is minimized while considering charging stations for recharging. We propose an efficient branch and cut framework and a three-phase hybrid heuristic algorithm that can efficiently solve a variety of instances. Extensive computational results and sensitivity analyses are performed to corroborate the efficiency of the proposed approach, both quantitatively and qualitatively.