A Hybrid Brain Storm Optimization Algorithm for Dynamic Vehicle Routing Problem With Time Windows
The vehicle routing problem (VRP) holds significant applications in logistics and distribution scenarios. This paper presents a hybrid brain storm optimization (BSO) algorithm for solving the dynamic vehicle routing problem with time windows (DVRPTW). The proposed hybrid BSO algorithm effectively ad...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10299621/ |
| _version_ | 1797357927650033664 |
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| author | Mingde Liu Qi Zhao Qi Song Yingbin Zhang |
| author_facet | Mingde Liu Qi Zhao Qi Song Yingbin Zhang |
| author_sort | Mingde Liu |
| collection | DOAJ |
| description | The vehicle routing problem (VRP) holds significant applications in logistics and distribution scenarios. This paper presents a hybrid brain storm optimization (BSO) algorithm for solving the dynamic vehicle routing problem with time windows (DVRPTW). The proposed hybrid BSO algorithm effectively addresses the dynamic emergence of new customers and minimizes the number of unserved customers by utilizing the repeated insertion algorithm. Furthermore, the algorithm uses BSO clustering operations to classify vehicle routes and facilitates mutual learning within and between classes through <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>-interchange. The intra-class similarity expedites solution convergence, while the inter-class difference expands the search space to avoid local optima. Finally, the quality of the solution is enhanced through the application of the 2-opt operation. To evaluate its performance, we compare the proposed algorithm with state-of-the-art algorithms using Lackner’s benchmark. The experimental results demonstrate that our algorithm significantly reduces the number of unserved customers. |
| first_indexed | 2024-03-08T14:52:34Z |
| format | Article |
| id | doaj.art-85afffd34f4043a0870f6e01ced2300b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T14:52:34Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-85afffd34f4043a0870f6e01ced2300b2024-01-11T00:01:42ZengIEEEIEEE Access2169-35362023-01-011112108712109510.1109/ACCESS.2023.332840410299621A Hybrid Brain Storm Optimization Algorithm for Dynamic Vehicle Routing Problem With Time WindowsMingde Liu0https://orcid.org/0000-0003-1874-3833Qi Zhao1https://orcid.org/0000-0003-4800-1136Qi Song2Yingbin Zhang3https://orcid.org/0000-0002-0444-5360China Telecom Research Institute, Guangzhou, ChinaDepartment of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, ChinaChina Telecom Research Institute, Guangzhou, ChinaChina Telecom Research Institute, Guangzhou, ChinaThe vehicle routing problem (VRP) holds significant applications in logistics and distribution scenarios. This paper presents a hybrid brain storm optimization (BSO) algorithm for solving the dynamic vehicle routing problem with time windows (DVRPTW). The proposed hybrid BSO algorithm effectively addresses the dynamic emergence of new customers and minimizes the number of unserved customers by utilizing the repeated insertion algorithm. Furthermore, the algorithm uses BSO clustering operations to classify vehicle routes and facilitates mutual learning within and between classes through <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>-interchange. The intra-class similarity expedites solution convergence, while the inter-class difference expands the search space to avoid local optima. Finally, the quality of the solution is enhanced through the application of the 2-opt operation. To evaluate its performance, we compare the proposed algorithm with state-of-the-art algorithms using Lackner’s benchmark. The experimental results demonstrate that our algorithm significantly reduces the number of unserved customers.https://ieeexplore.ieee.org/document/10299621/Brain storm optimizationdynamic vehicle routing problem with time windowsrepeated insertion |
| spellingShingle | Mingde Liu Qi Zhao Qi Song Yingbin Zhang A Hybrid Brain Storm Optimization Algorithm for Dynamic Vehicle Routing Problem With Time Windows IEEE Access Brain storm optimization dynamic vehicle routing problem with time windows repeated insertion |
| title | A Hybrid Brain Storm Optimization Algorithm for Dynamic Vehicle Routing Problem With Time Windows |
| title_full | A Hybrid Brain Storm Optimization Algorithm for Dynamic Vehicle Routing Problem With Time Windows |
| title_fullStr | A Hybrid Brain Storm Optimization Algorithm for Dynamic Vehicle Routing Problem With Time Windows |
| title_full_unstemmed | A Hybrid Brain Storm Optimization Algorithm for Dynamic Vehicle Routing Problem With Time Windows |
| title_short | A Hybrid Brain Storm Optimization Algorithm for Dynamic Vehicle Routing Problem With Time Windows |
| title_sort | hybrid brain storm optimization algorithm for dynamic vehicle routing problem with time windows |
| topic | Brain storm optimization dynamic vehicle routing problem with time windows repeated insertion |
| url | https://ieeexplore.ieee.org/document/10299621/ |
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