The paper introduces COMPASS, an algorithm for the Ordered Clustered Traveling Salesman Problem (OCTSP) that combines search with learning-accelerated routing through parallel sub-solvers. COMPASS improves solutions continuously with more compute, exploits clustered structure to achieve exact solutions in time exponential in cluster size, and outperforms existing methods. It works with general distance matrices, not just coordinate inputs, and scales to 100,000 synthetic nodes and 28,500 real e-commerce nodes, representing the largest reported routing solution over asymmetric distances.
By Ido Greenberg, Hugo Linsenmaier, Piotr Sielski, Shie Mannor, Alex Fender, Gal Chechik, Eli Meirom
arXiv:2609.35443v2 Announce Type: replace
Abstract: Large-scale routing problems are difficult to solve efficiently as their search spaces grow rapidly with problem size. Existing approaches primaril...
By Jiale Zhao, Sirui Mao, Zimu Chen, Wentao Yang, Zihan Wang, Xuefeng Huang, Junji Cheng, Liyuanjun Lai
arXiv:2607. 03694v1 Announce Type: new Abstract: Large-scale Capacitated Vehicle Routing Problems (CVRPs) are commonly solved by partitioning customers into smaller routing problems that can be optimized independently.
By Oguzhan Karaahmetoglu, Hyong Kim
arXiv:2503. 03137v3 Announce Type: replace Abstract: Constructive neural combinatorial optimization (NCO) offers a promising paradigm for solving vehicle routing problems (VRPs) by directly learning to construct approximate optimal solutions, thereby reducing reliance on expert knowledge for algorithm design.
By Changliang Zhou, Xi Lin, Zhenkun Wang, Qingfu Zhang
arXiv:2606. 19185v1 Announce Type: new Abstract: The Traveling Salesman Problem (TSP) is a cornerstone of combinatorial optimization and arises in many practical scenarios.
By Bolin Shen, Ziwei Huang, Zhiguang Cao, Yushun Dong
arXiv:2607. 09708v1 Announce Type: new Abstract: Solving large-scale instances of the Traveling Salesman Problem (TSP) exactly is computationally expensive.
By Tianfeng Chen, Xianyue Li