Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2024
Abstract
Electric vehicles (EVs) have gained considerable popularity, driven in part by an increased concern for the impact of automobile emissions on climate change. Electric vehicles (EVs) cover more than just conventional cars and trucks. They also include electric motorcycles, such as those produced by Gogoro, which serve as the primary mode of transportation for food and package delivery services in Taiwan. Consequently, the Electric Vehicle Routing Problem (EVRP) has emerged as an important variation of the Capacitated Vehicle Routing Problem (CVRP). In addition to the CVRP’s constraints, the EVRP requires vehicles to visit a charging station before the battery level is insufficient to continue service. EV battery consumption is linearly correlated to their weight. These additional constraints make the EVRP more challenging than the conventional CVRP. This study proposes an improved Harmony Search Algorithm (HSA), with performance validated by testing 24 available benchmark instances in the EVRP. This study also proposes a novel update mechanism in the improvement stage and a strategy to improve the routes with charging stations. The results show that in small and large instances, the proposed HSA improved the number of trips to the charging stations by 24% and 4.5%, respectively. These results were also verified using the Wilcoxon signed-rank significant test.
Keywords
vehicle routing problem, metaheuristic, electric vehicle routing problem, harmony search algorithm
Discipline
Theory and Algorithms
Research Areas
Intelligent Systems and Optimization
Publication
Energies
Volume
17
Issue
15
First Page
1
Last Page
22
ISSN
1996-1073
Identifier
10.3390/en17153716
Publisher
MDPI
Citation
MINANDA, Vanny; LIANG, Yun-Chia; CHEN, Angela H. L.; and GUNAWAN, Aldy.
Application of an improved harmony search algorithm on electric vehicle routing problems. (2024). Energies. 17, (15), 1-22.
Available at: https://ink.library.smu.edu.sg/sis_research/9278
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3390/en17153716