frvcpy: An Open-Source Solver for the Fixed Route Vehicle Charging Problem

Abstract

Electric vehicles offer a pathway to more sustainable transportation, but their adoption entails new challenges not faced by their petroleum-based counterparts. A difficult task in vehicle routing problems addressing these challenges is determining how to make good charging decisions for an electric vehicle traveling a given route. This is known as the fixed route vehicle charging problem. An exact and efficient algorithm for this task exists, but its implementation is sufficiently complex to deter researchers from adopting it. In this work we introduce frvcpy, an open-source Python package implementing this algorithm. Our aim with the package is to make it easier for researchers to solve electric vehicle routing problems, facilitating the development of optimization tools that may ultimately enable the mass adoption of electric vehicles. Summary of Contribution: This work describes a novel software tool for the vehicle routing community. The tool, frvcpy, addresses one of the primary challenges faced by the vehicle routing community when considering problems involving the adoption of electric vehicles (EVs): how to make optimal charging decisions. The state-of-the-art algorithm for solving these problems is sufficiently complex to deter researchers from using it, leading them to adopt less robust methods. frvcpy offers an easy-to-use, lightweight implementation of this algorithm, providing optimal solutions in low (∼5 ms) runtime. It is designed to be easily embedded in larger solution schemes for general EV routing problems, requiring minimal input, offering compatibility with the community standard file types, and offering access both through the command line and a Python API. The tool has thus far proven adaptable, having been used by researchers studying EV routing problems with novel constraints. Our aim with frvcpy is to make it easier for researchers to solve EV routing problems, facilitating the development of optimization tools that may contribute toward the mass adoption of electric vehicles.

Dynamic Ride-Hailing with Electric Vehicles

“…Given sequences for each vehicle, the subproblem must ensure they are energy feasible. To do so, we use a modified version of frvcpy (Kullman et al 2020), which implements the labeling algorithm developed by Froger et al (2019) to solve the Fixed Route Vehicle Charging Problem (FRVCP) (Montoya et al 2017). The FRVCP entails providing charging instructions (where to charge and to what amount) for an electric vehicle traversing a fixed sequence of customers.…”

Adaptive robust electric vehicle routing under energy consumption uncertainty

“…Many variants of EVRP have been studied and a recent solver was developed by Kullman et al (2021). Conrad and Figliozzi (2011) first proposed a recharging VRP allowing vehicle recharging en tour.…”

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

“…There are few freely available codes for the heuristics shared among the scientific community. An exception can be found in Kullman et al [79], who publish their Python package for solving the exact fixed-route vehicle-charging problem using the labeling algorithm. The recently developed general VRP solver has good potential for application in solving exact EV-VRP variants [80].…”