{"type":"ScholarlyArticle","title":"A Unified Knowledge Embedded Reinforcement Learning-based Framework for Generalized Capacitated Vehicle Routing Problems","authors":[{"name":"Wen Wang","slug":"wen-wang","given":"Wen","family":"Wang","affiliation":""},{"name":"Xiangchen Wu","slug":"xiangchen-wu","given":"Xiangchen","family":"Wu","affiliation":""},{"name":"Liang Wang","slug":"liang-wang","given":"Liang","family":"Wang","affiliation":""},{"name":"Hao Hu","slug":"hao-hu","given":"Hao","family":"Hu","affiliation":""},{"name":"Xianping Tao","slug":"xianping-tao","given":"Xianping","family":"Tao","affiliation":""}],"abstract":"The Capacitated Vehicle Routing Problem (CVRP) is a fundamental NP-hard problem with broad applications in logistics and transportation. Real-world CVRPs often involve diverse objectives and complex constraints, such as time windows or backhaul requirements, motivating the development of a unified solution framework.\r\nRecent reinforcement learning (RL) approaches have shown promise in combinatorial optimization, yet they rely on end-to-end learning and lack explicit problem-solving knowledge, limiting solution quality.\r\nIn this paper, we propose a knowledge-embedded framework inspired by the Route-First Cluster-Second heuristics. \r\nIt incorporates knowledge at two levels: (1) decomposing CVRPs into the route-first and cluster-second subproblems, and (2) leveraging dynamic programming to solve the second subproblem, whose results guide the RL-based constructive solver to solve the first problem.\r\nTo mitigate partial observability caused by problem decomposition, we introduce a unified history-enhanced context processing module. \r\nExtensive experiments show that this framework achieves superior solution quality compared with state-of-the-art learning-based methods, with a smaller gap to classical heuristics, demonstrating strong generalization across diverse CVRP variants.","doi":"10.24963/ijcai.2026/691","url":"https://ijcai.org/proceedings/2026/691","pdf":"https://ijcai.org/proceedings/2026/0691.pdf","markdown":"https://ijcai.org/proceedings/2026/691.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/691","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Planning and Scheduling","track":"Main Track","number":691,"pages":{"first":6208,"last":6216},"keywords":["Planning and Scheduling: Applications","Planning and Scheduling: Learning in planning and scheduling"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}