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http://hdl.handle.net/10397/112092
Title: | Knowledge-guided optimization for complex vehicle routing with 3D loading constraints | Authors: | Zhang, H Li, Q Yao, X |
Issue Date: | 2024 | Source: | Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2024, v. 15148, p. 133-148 | Abstract: | The split delivery vehicle routing problem with three-dimensional loading constraints (3L-SDVRP) intertwines complex routing and packing challenges. The current study addresses 3L-SDVRP using intelligent optimization algorithms, which iteratively evolve towards optimal solutions. A pivotal aspect of these algorithms is search operators that determine the search direction and the search step size. Effective operators significantly improve algorithmic performance. Traditional operators like swap, shift, and 2-opt fall short in complex scenarios like 3L-SDVRP, mainly due to their limited capacity to leverage domain knowledge. Additionally, the search step size is crucial: smaller steps enhance fine-grained search (exploitation), while larger steps facilitate exploring new areas (exploration). However, optimally balancing these step sizes remains an unresolved issue in 3L-SDVRP. To address this, we introduce an adaptive knowledge-guided insertion (AKI) operator. This innovative operator uses node distribution characteristics for adaptive node insertion, enhancing search abilities through domain knowledge integration and larger step sizes. Integrating AKI with the local search framework, we develop an adaptive knowledge-guided search (AKS) algorithm, which effectively balances exploitation and exploration by combining traditional neighbourhood operators for detailed searches with the AKI operator for broader exploration. Our experiments demonstrate that the AKS algorithm significantly outperforms the state-of-the-art method in solving various 3L-SDVRP instances. | Keywords: | Knowledge-guided optimization Packing Vehicle routing |
Publisher: | Springer | Journal: | Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) | ISBN: | 978-3-031-70054-5 (Softcover ) 978-3-031-70055-2 (eBook) |
ISSN: | 0302-9743 | EISSN: | 1611-3349 | DOI: | 10.1007/978-3-031-70055-2_9 | Description: | 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14–18, 2024 | Rights: | © The Author(s) 2024 This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The following publication Zhang, H., Li, Q., Yao, X. (2024). Knowledge-Guided Optimization for Complex Vehicle Routing with 3D Loading Constraints. In: Affenzeller, M., et al. Parallel Problem Solving from Nature – PPSN XVIII. PPSN 2024. Lecture Notes in Computer Science, vol 15148 (pp. 133-148). Springer, Cham is available at https://doi.org/10.1007/978-3-031-70055-2_9. |
Appears in Collections: | Conference Paper |
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