Just Initialize: A Training-Free Initialization Component for Large-Scale Routing Optimization
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Abstract (arXiv)
Large-scale routing problems are difficult to solve efficiently as their search spaces grow rapidly with problem size. Existing approaches primarily improve the optimization procedure itself, often at increasing computational cost. We instead shift the focus to a useful initialization that can be refined into a high-quality solution with limited downstream refinement. We propose Just Initialize, a training-free and solver-agnostic initialization component for large-scale routing optimization. Just Initialize compresses a large routing instance into a compact surrogate space, optimizes its global routing structure, and recovers the resulting solution as an optimization-friendly starting point in the original space. Extensive experiments on Traveling Salesman Problems (TSPs), Capacitated Vehicle Routing Problems (CVRPs), Vehicle Routing Problems with Time Windows (VRPTWs), and Prize-Collecting Traveling Salesman Problems (PCTSPs) demonstrate that Just Initialize achieves high-quality solutions comparable to or better than state-of-the-art methods while substantially reducing computational cost across instances ranging from 1K to 100K nodes, including an average speedup of approximately 70×, sub-second runtimes on 10K-node instances, and runtimes within tens of seconds on 100K-node instances.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 47 references that are in the map. First posted on arXiv 28 Sep 2026.
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References · 22
- 2018Attention, Learn to Solve Routing Problems!cited by 656
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- 2023DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimizationcited by 221
- 2023Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalizationcited by 142
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- 2020Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhoodcited by 125
- 2022Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimizationcited by 119
- 2021Learning to Delegate for Large-scale Vehicle Routingcited by 109
- 2021NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problemcited by 103
- 2021Deep Policy Dynamic Programming for Vehicle Routing Problemscited by 94
- 2021Learning Collaborative Policies to Solve NP-hard Routing Problemscited by 86
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- 2023GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-timecited by 85
- 2023DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimizationcited by 78
- 2022Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillationcited by 62
- 2024UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problemscited by 47
- 2024INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformercited by 44
- 2023H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problemcited by 43
- 2024Self-Improved Learning for Scalable Neural Combinatorial Optimizationcited by 29
- 2025Improving Generalization of Neural Combinatorial Optimization for Vehicle Routing Problems via Test-Time Projection Learningcited by 12
- 2025Learning to Insert for Constructive Neural Vehicle Routing Solvercited by 10
- 2021Constrained Local Search for Last-Mile Routingcited by 7
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