| Title: | A combined genetic algorithm and A* search algorithm for the electric vehicle routing problem with time windows |
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| Authors: | ID Wang, D. L. (Author)
ID Ding, A. (Author)
ID Chen, G. L. (Author)
ID Zhang, L. (Author) |
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| Files: |  APEM18-4_403-416.pdf (1,19 MB)
MD5: 38E894260792C60AFF57DD25DACF1C80
 https://apem-journal.org/Archives/2023/APEM18-4_403-416.pdf
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | With growing environmental concerns, the focus on greenhouse gases (GHG) emissions in transportation has increased, and the combination of smart microgrids and electric vehicles (EVs) brings a new opportunity to solve this problem. Electric vehicle routing problem with time windows (EVRPTW) is an extension of the vehicle routing problem (VRP) problem, which can reach the combination of smart microgrids and EVs precisely by scheduling the EVs. However, the current genetic algorithm (GA) for solving this problem can easily fall into the dilemma of local optimization and slow iteration speed. In this paper, we present an integer hybrid planning model that introduces time of use and area price to enhance realism. We propose the GA-A* algorithm, which combines the A* algorithm and GA to improve global search capability and iteration speed. We conducted experiments on 16 benchmark cases, comparing the GA-A* algorithm with traditional GA and other search algorithms, results demonstrate significant enhancements in searchability and optimal solutions. In addition, we measured the grid load, and the model implements the vehicle-to-grid (V2G) mode, which serves as peak shaving and valley filling by integrating EVs into the grid for energy delivery and exchange through battery swapping. This research, ranging from model optimization to algorithm improvement, is an important step towards solving the EVRPTW problem and improving the environment. |
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| Keywords: | vehicle routing problem (VRP), electric vehicle, optimization, time windows, spatiotemporal electricity price, smart microgrids, genetic algorithm (GA), A* search algorithm, GA-A* algorithm |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 25.09.2023 |
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| Article acceptance date: | 21.11.2023 |
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| Publication date: | 28.12.2023 |
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| Publisher: | Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering |
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| Year of publishing: | 2023 |
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| Number of pages: | str. 403-416 |
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| Numbering: | Vol. 18, no. 4 |
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| PID: | 20.500.12556/DKUM-97125  |
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| UDC: | 621.31:519.17 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 268945667 |
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| DOI: | 10.14743/apem2023.4.481 |
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| Copyright: | Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
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| Publication date in DKUM: | 19.02.2026 |
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| Views: | 200 |
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| Downloads: | 1 |
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| Metadata: |    |
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| Categories: | Misc.
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