An Adaptive Large Neighborhood Search for a Green Vehicle Routing Problem with Depot Sharing
In urban logistics distribution, vehicle carbon emissions during the distribution process significantly contribute to environmental pollution. While developing green logistics is critical for the sustainable growth of the logistics industry, existing studies often overlook the potential benefits of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/2/214 |
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| author | Zixuan Wu Ping Lou Jianmin Hu Yuhang Zeng Chuannian Fan |
| author_facet | Zixuan Wu Ping Lou Jianmin Hu Yuhang Zeng Chuannian Fan |
| author_sort | Zixuan Wu |
| collection | DOAJ |
| description | In urban logistics distribution, vehicle carbon emissions during the distribution process significantly contribute to environmental pollution. While developing green logistics is critical for the sustainable growth of the logistics industry, existing studies often overlook the potential benefits of depot sharing among enterprises. By enabling depots belonging to different enterprises to be shared, it would shorten the distance traveled by vehicles returning to depots and reduce carbon emissions. And it would also reduce the number of depots being built. Therefore, a green vehicle routing problem with depot sharing is presented in the paper. To solve this problem, an improved adaptive large neighborhood search algorithm is presented, in which the Split strategy and two new operators are proposed to enhance solution quality and computational efficiency. Extensive numerical experiments are conducted on instances of varying scales to evaluate this algorithm, and also demonstrate its effectiveness and efficiency. Furthermore, the experimental results demonstrate that depot sharing significantly reduces carbon emissions, achieving an average optimization rate of 10.1% across all instances compared to returning to the original depot. |
| format | Article |
| id | doaj-art-8ed34b0c50dc4775848c834bdd993846 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-8ed34b0c50dc4775848c834bdd9938462025-01-24T13:39:46ZengMDPI AGMathematics2227-73902025-01-0113221410.3390/math13020214An Adaptive Large Neighborhood Search for a Green Vehicle Routing Problem with Depot SharingZixuan Wu0Ping Lou1Jianmin Hu2Yuhang Zeng3Chuannian Fan4School of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Hubei University of Economics, Wuhan 430205, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaIn urban logistics distribution, vehicle carbon emissions during the distribution process significantly contribute to environmental pollution. While developing green logistics is critical for the sustainable growth of the logistics industry, existing studies often overlook the potential benefits of depot sharing among enterprises. By enabling depots belonging to different enterprises to be shared, it would shorten the distance traveled by vehicles returning to depots and reduce carbon emissions. And it would also reduce the number of depots being built. Therefore, a green vehicle routing problem with depot sharing is presented in the paper. To solve this problem, an improved adaptive large neighborhood search algorithm is presented, in which the Split strategy and two new operators are proposed to enhance solution quality and computational efficiency. Extensive numerical experiments are conducted on instances of varying scales to evaluate this algorithm, and also demonstrate its effectiveness and efficiency. Furthermore, the experimental results demonstrate that depot sharing significantly reduces carbon emissions, achieving an average optimization rate of 10.1% across all instances compared to returning to the original depot.https://www.mdpi.com/2227-7390/13/2/214green vehicle routing problemdepot sharingcarbon emissionadaptive large neighborhood search algorithm |
| spellingShingle | Zixuan Wu Ping Lou Jianmin Hu Yuhang Zeng Chuannian Fan An Adaptive Large Neighborhood Search for a Green Vehicle Routing Problem with Depot Sharing Mathematics green vehicle routing problem depot sharing carbon emission adaptive large neighborhood search algorithm |
| title | An Adaptive Large Neighborhood Search for a Green Vehicle Routing Problem with Depot Sharing |
| title_full | An Adaptive Large Neighborhood Search for a Green Vehicle Routing Problem with Depot Sharing |
| title_fullStr | An Adaptive Large Neighborhood Search for a Green Vehicle Routing Problem with Depot Sharing |
| title_full_unstemmed | An Adaptive Large Neighborhood Search for a Green Vehicle Routing Problem with Depot Sharing |
| title_short | An Adaptive Large Neighborhood Search for a Green Vehicle Routing Problem with Depot Sharing |
| title_sort | adaptive large neighborhood search for a green vehicle routing problem with depot sharing |
| topic | green vehicle routing problem depot sharing carbon emission adaptive large neighborhood search algorithm |
| url | https://www.mdpi.com/2227-7390/13/2/214 |
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