Multi-task evolutionary optimization of multi-echelon location routing problems via a hierarchical fuzzy graph
Abstract Multi-echelon location-routing problems (ME-LRPs) deal with determining the location of facilities and the routes of vehicles on multi-echelon routing tasks. Since the assignment relationship in multi-echelon routing tasks is uncertain and varying, ME-LRPs are very challenging to solve, esp...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-06-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-01109-0 |
| _version_ | 1797647145563586560 |
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| author | Xueming Yan Yaochu Jin Xiaohua Ke Zhifeng Hao |
| author_facet | Xueming Yan Yaochu Jin Xiaohua Ke Zhifeng Hao |
| author_sort | Xueming Yan |
| collection | DOAJ |
| description | Abstract Multi-echelon location-routing problems (ME-LRPs) deal with determining the location of facilities and the routes of vehicles on multi-echelon routing tasks. Since the assignment relationship in multi-echelon routing tasks is uncertain and varying, ME-LRPs are very challenging to solve, especially when the number of the echelons increases. In this study, the ME-LRP is formulated as a hierarchical fuzzy graph, in which high-order fuzzy sets are constructed to represent the uncertain assignment relationship as different routing tasks and cross-task operators are used for routing task selection. Then, an evolutionary multi-tasking optimization algorithm is designed to simultaneously solve the multiple routing tasks. To alleviate negative transfer between the different routing tasks, multi-echelon assignment information is considered together with associated routing task selection in multi-tasking evolution optimization. The experimental results on multi-echelon routing benchmark problems demonstrate the competitiveness of the proposed method. |
| first_indexed | 2024-03-11T15:12:05Z |
| format | Article |
| id | doaj.art-c10c558ab3bd4b4ebd27f2a6ad3c0a27 |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-11T15:12:05Z |
| publishDate | 2023-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-c10c558ab3bd4b4ebd27f2a6ad3c0a272023-10-29T12:41:42ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-06-01966845686210.1007/s40747-023-01109-0Multi-task evolutionary optimization of multi-echelon location routing problems via a hierarchical fuzzy graphXueming Yan0Yaochu Jin1Xiaohua Ke2Zhifeng Hao3School of Information Science and Technology, Guangdong University of Foreign StudiesFaculty of Technology, Bielefeld UniversitySchool of Information Science and Technology, Guangdong University of Foreign StudiesDepartment of Mathematics, College of Science, Shantou UniversityAbstract Multi-echelon location-routing problems (ME-LRPs) deal with determining the location of facilities and the routes of vehicles on multi-echelon routing tasks. Since the assignment relationship in multi-echelon routing tasks is uncertain and varying, ME-LRPs are very challenging to solve, especially when the number of the echelons increases. In this study, the ME-LRP is formulated as a hierarchical fuzzy graph, in which high-order fuzzy sets are constructed to represent the uncertain assignment relationship as different routing tasks and cross-task operators are used for routing task selection. Then, an evolutionary multi-tasking optimization algorithm is designed to simultaneously solve the multiple routing tasks. To alleviate negative transfer between the different routing tasks, multi-echelon assignment information is considered together with associated routing task selection in multi-tasking evolution optimization. The experimental results on multi-echelon routing benchmark problems demonstrate the competitiveness of the proposed method.https://doi.org/10.1007/s40747-023-01109-0Multi-echelon location-routing problemsHierarchical fuzzy graphMulti-task evolutionary optimizationCross-task evolution |
| spellingShingle | Xueming Yan Yaochu Jin Xiaohua Ke Zhifeng Hao Multi-task evolutionary optimization of multi-echelon location routing problems via a hierarchical fuzzy graph Complex & Intelligent Systems Multi-echelon location-routing problems Hierarchical fuzzy graph Multi-task evolutionary optimization Cross-task evolution |
| title | Multi-task evolutionary optimization of multi-echelon location routing problems via a hierarchical fuzzy graph |
| title_full | Multi-task evolutionary optimization of multi-echelon location routing problems via a hierarchical fuzzy graph |
| title_fullStr | Multi-task evolutionary optimization of multi-echelon location routing problems via a hierarchical fuzzy graph |
| title_full_unstemmed | Multi-task evolutionary optimization of multi-echelon location routing problems via a hierarchical fuzzy graph |
| title_short | Multi-task evolutionary optimization of multi-echelon location routing problems via a hierarchical fuzzy graph |
| title_sort | multi task evolutionary optimization of multi echelon location routing problems via a hierarchical fuzzy graph |
| topic | Multi-echelon location-routing problems Hierarchical fuzzy graph Multi-task evolutionary optimization Cross-task evolution |
| url | https://doi.org/10.1007/s40747-023-01109-0 |
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