Routing electric vehicles with a single recharge per route

Abstract

AbstractDriven by environmental considerations, regulations on vehicle emissions, and the offer of major subsidies, electric commercial vehicles (ECVs) are receiving ever stronger attention in logistics companies. Route planning for ECV fleets requires consideration of the special characteristics of ECVs, like limited driving range and the potential need to recharge en route at dedicated recharging stations. From a practical viewpoint, the number of recharge operations of each vehicle can very often be restricted to one recharge per route because (i) typical route distances in the most important application areas of ECVs, like small package shipping and food or beverage distribution, do not require more than one recharge given the current driving range of ECVs, and (ii) operations managers are very reluctant to plan vehicle routes with two or more recharges because recharging operations are perceived as unproductive idle times. We develop a simple hybrid of large neighborhood search and granular tabu search to solve the resulting electric vehicle‐routing problem with time windows and single recharge (EVRPTWS), considering the possibility of both full and partial recharge. The heuristic works on routes represented as customer sequences, and recharge operations are implicitly considered by determining the recharging position in the route, the recharging station to visit, and the amount to be recharged in optimal fashion. We discuss how our algorithm can be extended to handle nonlinear recharging times, different recharging times per station, and time‐dependent waiting times at stations. In numerical studies on EVRPTWS instances from the literature, the method provides optimal or near‐optimal solutions for instances with up to 100 customers within reasonable runtimes. Additional studies investigate the cost savings potential of partial recharges in comparison to full recharges in the presence of time‐window constraints, and examine the factors that influence this cost saving potential.