Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

9-2020

Abstract

Electric Vehicles (EVs) and charging infrastructure are starting to become commonplace in major cities around the world. For logistics providers to adopt an EV fleet, there are many factors up for consideration, such as route planning for EVs with limited travel range as well as long-term planning of fleet size. In this paper, we present a genetic algorithm to perform route planning that minimises the number of vehicles required. Specifically, we discuss the challenges on the violations of constraints in the EV routing problem (EVRP) arising from applying genetic algorithm operators. To overcome the challenges, techniques specific to addressing the infeasibility of solutions are discussed. We test our genetic algorithm against EVRP benchmarks and show that it outperforms them for most problem instances on both the number of vehicles as well as total time traveled.

Keywords

Electric Vehicle Routing Problem, Genetic algorithm

Discipline

Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

Computational Logistics: ICCL 2020: September 28-30, Enschede, Netherlands: Proceedings

Volume

12433

First Page

200

Last Page

214

ISBN

9783030597474

Identifier

10.1007/978-3-030-59747-4_13

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-030-59747-4_13

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