The Electric Vehicle Routing Problem with Capacitated Charging Stations

Abstract

Electric vehicle routing problems (E-VRPs) deal with routing a fleet of electric vehicles (EVs) to serve a set of customers while minimizing an operational criterion, for example, cost or time. The feasibility of the routes is constrained by the autonomy of the EVs, which may be recharged along the route. Much of the E-VRP research neglects the capacity of charging stations (CSs) and thus implicitly assumes that an unlimited number of EVs can be simultaneously charged at a CS. In this paper, we model and solve E-VRPs considering these capacity restrictions. In particular, we study an E-VRP with nonlinear charging functions, multiple charging technologies, en route charging, and variable charging quantities while explicitly accounting for the number of chargers available at privately managed CSs. We refer to this problem as the E-VRP with nonlinear charging functions and capacitated stations (E-VRP-NL-C). We introduce a continuous-time model formulation for the problem. We then introduce an algorithmic framework that iterates between two main components: (1) the route generator, which uses an iterated local search algorithm to build a pool of high-quality routes, and (2) the solution assembler, which applies a branch-and-cut algorithm to combine a subset of routes from the pool into a solution satisfying the capacity constraints. We compare four assembly strategies on a set of instances. We show that our algorithm effectively deals with the E-VRP-NL-C. Furthermore, considering the uncapacitated version of the E-VRP-NL-C, our solution method identifies new best-known solutions for 80 of 120 instances.

A matheuristic for a 2-echelon vehicle routing problem with capacitated satellites and reverse flows

“…Grangier et al (2019) consider a VRP with cross-docking with a limited number of docks that can be used simultaneously. Froger et al (2017) integrate a limited number of chargers at charging stations in electric VRPs. In the two latter contributions, each vehicle consumes one unit of the resource per time unit.…”

Column Generation for Solving the Electric Autonomous Dial-a-Ride Problem

“…Schneider, Stenger, and Goeke (2014) Many works relax the full recharging assumption to a partial recharging policy, e.g., (Bruglieri et al 2015, Keskin and C ¸atay 2016, Desaulniers et al 2016, Duman, Taş, and C ¸atay 2021, Ceselli et al 2021. The majority of these articles develop meta-heuristic algorithms to solve the problem, e.g., (Felipe et al 2014, Hiermann et al 2019, Montoya et al 2017, Froger et al 2017, while some of them propose exact methods. The representative work is Desaulniers et al (2016), where the authors investigate four different recharging policies: full recharging plus single/multiple visits to recharging stations and partial recharging plus single/multiple visits to recharging stations.…”

Survey of charging management and infrastructure planning for electrified demand-responsive transport systems: Methodologies and recent developments

“…Some authors use freely available ride data from ridehailing companies to test the performance of proposed operational policies for dynamic ride-hailing systems [54,55]. Froger et al [78] provide 120 test instances for EV-VRP with non-linear charging functions and capacitated charging stations. The exact up-to-date and heuristic solutions for EV-VRPTW are provided in Kucukoglu et al [14].…”