A local search with chain search path strategy for real-world many-objective vehicle routing problem
Abstract This article focuses on a new application-oriented variant of vehicle routing problem. This problem comes from the daily distribution scenarios of a real-world logistics company. It is a large-scale (with customer sizes up to 2000), many-objective (with six objective functions) NP-hard prob...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-03-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01825-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850208493750976512 |
|---|---|
| author | Ying Zhou Lingjing Kong Hui Wang Yiqiao Cai Shaopeng Liu |
| author_facet | Ying Zhou Lingjing Kong Hui Wang Yiqiao Cai Shaopeng Liu |
| author_sort | Ying Zhou |
| collection | DOAJ |
| description | Abstract This article focuses on a new application-oriented variant of vehicle routing problem. This problem comes from the daily distribution scenarios of a real-world logistics company. It is a large-scale (with customer sizes up to 2000), many-objective (with six objective functions) NP-hard problem with six constraints. Then, a local search with chain search path strategy (LS-CSP) is proposed to effectively solve the problem. It is a decomposition-based algorithm. First, the considered problem is decomposed into multiple single-objective subproblems. Then, local search is applied to solve these subproblems one by one. The advantage of the LS-CSP lies in a chain search path strategy, which is designed for determining the order of solving the subproblems. This strategy can help the algorithm find a high-quality solution set quickly. Finally, to assess the performance of the proposed LS-CSP, three instance sets containing 132 instances are provided, and four state-of-the-art decomposition-based approaches are adopted as the competitors. Experimental results show the effectiveness of the proposed algorithm for the considered problem. |
| format | Article |
| id | doaj-art-af8bada9722c4e3d8fe7f89c23112bc5 |
| institution | OA Journals |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-af8bada9722c4e3d8fe7f89c23112bc52025-08-20T02:10:13ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-03-0111413210.1007/s40747-025-01825-9A local search with chain search path strategy for real-world many-objective vehicle routing problemYing Zhou0Lingjing Kong1Hui Wang2Yiqiao Cai3Shaopeng Liu4School of Artificial Intelligence, Shenzhen Institute of Information TechnologySchool of Artificial Intelligence, Shenzhen Institute of Information TechnologySchool of Computer and Software Engineering, Shenzhen Institute of Information TechnologyCollege of Computer Science and Technology, Huaqiao UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversityAbstract This article focuses on a new application-oriented variant of vehicle routing problem. This problem comes from the daily distribution scenarios of a real-world logistics company. It is a large-scale (with customer sizes up to 2000), many-objective (with six objective functions) NP-hard problem with six constraints. Then, a local search with chain search path strategy (LS-CSP) is proposed to effectively solve the problem. It is a decomposition-based algorithm. First, the considered problem is decomposed into multiple single-objective subproblems. Then, local search is applied to solve these subproblems one by one. The advantage of the LS-CSP lies in a chain search path strategy, which is designed for determining the order of solving the subproblems. This strategy can help the algorithm find a high-quality solution set quickly. Finally, to assess the performance of the proposed LS-CSP, three instance sets containing 132 instances are provided, and four state-of-the-art decomposition-based approaches are adopted as the competitors. Experimental results show the effectiveness of the proposed algorithm for the considered problem.https://doi.org/10.1007/s40747-025-01825-9Many-objective optimizationRich vehicle routing problemLarge-scale problemLocal search |
| spellingShingle | Ying Zhou Lingjing Kong Hui Wang Yiqiao Cai Shaopeng Liu A local search with chain search path strategy for real-world many-objective vehicle routing problem Complex & Intelligent Systems Many-objective optimization Rich vehicle routing problem Large-scale problem Local search |
| title | A local search with chain search path strategy for real-world many-objective vehicle routing problem |
| title_full | A local search with chain search path strategy for real-world many-objective vehicle routing problem |
| title_fullStr | A local search with chain search path strategy for real-world many-objective vehicle routing problem |
| title_full_unstemmed | A local search with chain search path strategy for real-world many-objective vehicle routing problem |
| title_short | A local search with chain search path strategy for real-world many-objective vehicle routing problem |
| title_sort | local search with chain search path strategy for real world many objective vehicle routing problem |
| topic | Many-objective optimization Rich vehicle routing problem Large-scale problem Local search |
| url | https://doi.org/10.1007/s40747-025-01825-9 |
| work_keys_str_mv | AT yingzhou alocalsearchwithchainsearchpathstrategyforrealworldmanyobjectivevehicleroutingproblem AT lingjingkong alocalsearchwithchainsearchpathstrategyforrealworldmanyobjectivevehicleroutingproblem AT huiwang alocalsearchwithchainsearchpathstrategyforrealworldmanyobjectivevehicleroutingproblem AT yiqiaocai alocalsearchwithchainsearchpathstrategyforrealworldmanyobjectivevehicleroutingproblem AT shaopengliu alocalsearchwithchainsearchpathstrategyforrealworldmanyobjectivevehicleroutingproblem AT yingzhou localsearchwithchainsearchpathstrategyforrealworldmanyobjectivevehicleroutingproblem AT lingjingkong localsearchwithchainsearchpathstrategyforrealworldmanyobjectivevehicleroutingproblem AT huiwang localsearchwithchainsearchpathstrategyforrealworldmanyobjectivevehicleroutingproblem AT yiqiaocai localsearchwithchainsearchpathstrategyforrealworldmanyobjectivevehicleroutingproblem AT shaopengliu localsearchwithchainsearchpathstrategyforrealworldmanyobjectivevehicleroutingproblem |