scholarly journals Adaptive Elitist Genetic Algorithm With Improved Neighbor Routing Initialization for Electric Vehicle Routing Problems

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 16661-16671
Author(s):  
Yanfei Zhu ◽  
Kwang Y. Lee ◽  
Yonghua Wang
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problems ◽  
Routing Problems

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

2021 ◽  
Author(s):  
Nicholas D. Kullman ◽  
Aurelien Froger ◽  
Jorge E. Mendoza ◽  
Justin C. Goodson
Keyword(s):  
Open Source ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Software Tool ◽  
Sustainable Transportation ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Vehicle Charging ◽  
Community Standard

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.

A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems

2012 ◽  
Vol 60 (3) ◽  
pp. 611-624 ◽  
Author(s):  
Thibaut Vidal ◽  
Teodor Gabriel Crainic ◽  
Michel Gendreau ◽  
Nadia Lahrichi ◽  
Walter Rei
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Hybrid Genetic Algorithm ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Periodic Vehicle Routing
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