Optimal electric vehicle routing for minimizing electrical energy consumption based on hybrid genetic algorithm

Hong Chen, Tomohiro Murata

Research output: Contribution to journalConference article

Abstract

— This paper presents a novelty model for solving a routing problem for delivery service using EV trucks called electric vehicle routing problem (EVRP). We also present a meta-heuristic algorithm by combining genetic algorithm and Tabu Search algorithm, to solve the EVRP. The difficulty of using a EV truck for a good delivery service is finding out an optimal route in a huge number of rotes. additionally, it also need to consider the short driving distance problem because of battery limited of EV. The formulation present was based on a mixed integer programming formulation, objective to minimum the total electrical consumption and multiple constraints, which considered the electric recovery in a gradient road and arrange the loading assignment, to improve the electrical consumption efficiency. additionally, the electrical consumption balance of every trucks was considered. It good to reduce the charging operation time and improve the operations efficiency for this problem. We designed and executed five experiments to evaluate our formulation and algorithm, verified the effectiveness of our idea in EVRP problem. Experiments show that, the total electrical consumption calculated by proposed EVRP model could be improved 2.3% than a conventional VRP model, and the operations time balance objective made 48.3%’s reducing of charging operation time.

Original languageEnglish
Pages (from-to)526-531
Number of pages6
JournalLecture Notes in Engineering and Computer Science
Volume2239
Publication statusPublished - 2019 Jan 1
Event2019 International MultiConference of Engineers and Computer Scientists, IMECS 2019 - Kowloon, Hong Kong
Duration: 2019 Mar 132019 Mar 15

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Keywords

  • Genetic algorithm
  • Index electric vehicle routing problem
  • Optimization

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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