A large neighborhood search-based matheuristic for the load-dependent electric vehicle routing problem with time windows

Range anxiety of electric vehicles (EVs) still poses a major barrier in their adoption in the logistics operations despite the advancements in the battery technology. The need for recharging the battery during the day brings additional complexities to the operational planning of commercial EVs in last mile deliveries. The driving range of an EV may vary according to different factors including ambient temperature, weight, speed, acceleration/deceleration, and the road profile. In this study, we revisit the well-known electric vehicle routing problem with time windows by taking into account the weight of the load carried. Cargo weight may play a crucial role in the operational efficiency of the EVs since it may affect the energy consumption significantly. We first present two alternative mathematical formulations of the problem and test their performances on small-size instances that can be solved using a commercial solver. Next, we develop a matheuristic approach that integrates an optimal repair procedure in the large neighbourhood search method and validate its performance. Then, we present an extensive numerical study to investigate the influence of load on the routing decisions. Our results show that cargo weight may create substantial changes in the route plans and fleet size, and neglecting it may cause severe disruptions in service and increase the costs.

Automated summary

Range anxiety is a major barrier to the adoption of electric vehicles in logistics operations. The weight of the load carried plays a crucial role in the operational efficiency and routing decisions of electric vehicles.