{"type":"ScholarlyArticle","title":"Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy","authors":[{"name":"Chengrui Gao","slug":"chengrui-gao","given":"Chengrui","family":"Gao","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China; School of Artificial Intelligence , Nanjing University , China"},{"name":"Haopu Shang","slug":"haopu-shang","given":"Haopu","family":"Shang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China; School of Artificial Intelligence , Nanjing University , China"},{"name":"Ke Xue","slug":"ke-xue","given":"Ke","family":"Xue","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China; School of Artificial Intelligence , Nanjing University , China"},{"name":"Dong Li","slug":"dong-li","given":"Dong","family":"Li","affiliation":"Huawei Noah's Ark Lab"},{"name":"Chao Qian","slug":"chao-qian","given":"Chao","family":"Qian","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China; School of Artificial Intelligence , Nanjing University , China"}],"abstract":"Machine learning has been adapted to help solve NP-hard combinatorial optimization problems. One prevalent way is learning to construct solutions by deep neural networks, which has been receiving more and more attention due to the high efficiency and less requirement for expert knowledge. However, many neural construction methods for Vehicle Routing Problems~(VRPs) focus on synthetic problem instances with specified node distributions and limited scales, leading to poor performance on real-world problems which usually involve complex and unknown node distributions together with large scales. To make neural VRP solvers more practical, we design an auxiliary policy that learns from the local transferable topological features, named local policy, and integrate it with a typical construction policy (which learns from the global information of VRP instances) to form an ensemble policy. With joint training, the aggregated policies perform cooperatively and complementarily to boost generalization. The experimental results on two well-known benchmarks, TSPLIB and CVRPLIB, of travelling salesman problem and capacitated VRP show that the ensemble policy significantly improves both cross-distribution and cross-scale generalization performance, and even performs well on real-world problems with several thousand nodes.","doi":"10.24963/ijcai.2024/764","url":"https://ijcai.org/proceedings/2024/764","pdf":"https://ijcai.org/proceedings/2024/0764.pdf","markdown":"https://ijcai.org/proceedings/2024/764.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/764","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Search","track":"Main Track","number":764,"pages":{"first":6914,"last":6922},"keywords":["Machine Learning: ML: Reinforcement learning","Search: S: Search and machine learning","Search: S: Combinatorial search and optimisation"],"videos":[],"references":[{"position":1,"raw":"[Applegate et al., 2006] David Applegate, Robert Bixby, Vasek Chvatal, and William Cook. 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