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:2602. 23092v2 Announce Type: replace Abstract: The Capacitated Vehicle Routing Problem (CVRP), a fundamental combinatorial optimization challenge, focuses on optimizing fleet operations under vehicle capacity constraints.
By Zhuoliang Xie, Fei Liu, Zhenkun Wang, Qingfu Zhang
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
SynthCharge is a parametric generator that creates diverse, feasibility‑screened instances of the electric vehicle routing problem with time windows (EVRPTW). It produces instances ranging from 5 to 100 customers (up to 500 in theory) with adaptive energy capacity scaling and range‑aware charging station placement, filtering out unsolvable cases via a fast feasibility screening process. This dynamic benchmarking infrastructure enables systematic evaluation of learning‑based routing and data‑driven approaches.
By Mertcan Daysalilar, Fuat Uyguroglu, Gabriel Nicolosi, Adam Meyers
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
RouteRepair is a method that diagnoses specific weaknesses in large language model (LLM)-generated routing heuristics by evaluating performance at the instance level and then applies targeted modifications to the heuristic components that are failing, while preserving components that already perform well. It combines routing evidence, solver behavior, and program context to set bounded repair objectives and validates each change through matched parent-child evaluation of failure recovery and collateral degradation. Experiments on the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP) show significant reductions in optimality gaps and route costs, demonstrating that failure-aware, evidence-constrained refinement can improve routing heuristics on difficult instances while maintaining performance on easier cases.
By Binghao Ji, Di Huang, Jiahui Fang, Zhiyuan Liu