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
arXiv:2609.25149v1 Announce Type: new
Abstract: Solving large-scale instances of the Traveling Salesman Problem (TSP) exactly is computationally expensive. Researchers often employ graph sparsificati...
By Tianfeng Chen, Xianyue Li
arXiv:2606. 07587v1 Announce Type: new Abstract: LLM routing has become a popular approach to improve the cost-quality trade-off of LLM services by dynamically selecting a model for each query.
By Yifan Lu, Qiyue Zhang, Shenrun Zhang, Zhibo Yu, Zhuang Wang, Hanjie Chen, Jiarong Xing
arXiv:2606. 31820v1 Announce Type: new Abstract: Large-scale capacitated vehicle routing problems (CVRPs) are commonly addressed using cluster-first route-second (CFRS) approaches that split a routing instance into smaller, computationally tractable subproblems.
By Oguzhan Karaahmetoglu (Carnegie Mellon University), Hyong Kim (Carnegie Mellon University)
arXiv:2607. 19072v1 Announce Type: new Abstract: This paper introduces a self-supervised pretraining framework for graph combinatorial optimization specifically designed to address the nature of routing problems like the Traveling Salesman Problem.
By David Aguado, Daniel Fuertes, Carlos R. del-Blanco, Fernando Jaureguizar
arXiv:2606. 06618v1 Announce Type: cross Abstract: How can we plan long-horizon routes that reach designated goals, visit required waypoints, and remain short when only short-horizon offline trajectories are available?
By Jungmin Seo, Jaesik Park
The paper introduces a fine‑grain GPU implementation of the partition phase of the Generalized Partition Crossover (GPX) for large‑scale Traveling Salesman Problem (TSP) instances. By reformulating GPX partitioning as a graph‑parallel problem with coalesced memory layouts, ghost‑node transformations, and connected‑component analysis, the authors parallelize key operations such as union of parent tours, splitting of degree‑four vertices, deletion of common edges, and component identification using CUDA. Experiments on instances from 10,000 to 2 million cities show speedups between 48× and 625× over a naive sequential CPU implementation while significantly reducing memory overhead.
By Swetha Varadarajan, Darrell Whitley