Layered Graph Models for the Electric Vehicle Routing Problem With Nonlinear Charging Functions

• Published in: Networks Vol. 85; no. 1; pp. 113 - 126
• Main Authors: Bruni, Maria Elena, Cubillos, Maximiliano, Jabali, Ola
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2025; Wiley Subscription Services, Inc
• Subjects: Algorithms; Approximation; Batteries; charging policies; charging stations; Computing time; Customers; Decisions; Electric arcs; Electric vehicle charging; Electric vehicles; Energy consumption; Graph representations; Graphical representations; layered graphs; nonlinear charging function; Optimization; State of charge; Vehicle routing
• ISSN: 0028-3045; 1097-0037; 1097-0037
• DOI: 10.1002/net.22251

Electric vehicle routing problems (EVRPs) involve the routing of a fleet of electric vehicles (EVs) to visit a set of customers while typically minimizing the total travel and charging time. Due to their limited autonomy, EVs may need to recharge their batteries en‐route at charging stations (CSs). Thus, routing decisions also include which CSs to visit, and how much energy to charge during those visits. These decisions are compounded by the fact that charging times follow a nonlinear charging function with respect to the EV's state of charge (SoC). We propose a layered graph representation for the EVRP with nonlinear charging functions (EVRP‐NL). Specifically, the layers correspond to discretized SoC values. Therefore, the arcs' energy consumption is approximated to match those values. We develop two compact formulations based on the layered graph. Furthermore, we introduced two charging policies that facilitate aligning charging duration with practical considerations. Computational results demonstrate the effectiveness of our formulations. Our best formulation effectively handles instances with up to 40 customers. On those instances, compared to the state‐of‐the‐art compact formulation, our formulation solves 13 more instances to optimality with less than half of the computational time. Considering instances solved by both formulations to optimally, the approximation entailed by our formulation yields a 0.94% deviation on average. Since our best performing formulation is compact, it may be readily used by a broad audience. Furthermore, as the majority of algorithms for the EVPR and its variants are heuristics, our formulation could be beneficial in evaluating the performance of these methods.