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Two-Stage Learning to Branch in Branch-Price-and-Cut Algorithms for Solving Vehicle Routing Problems Exactly

Zhengzhong You et al.

Operations Research2026https://doi.org/10.1287/opre.2023.0615article
FT50UTD24AJG 4*ABDC A*
Weight
0.50

Abstract

Smarter Branching Speeds Up Leading Exact Vehicle Routing Solvers Researchers have introduced a learning-based branching strategy that substantially accelerates exact algorithms for vehicle routing problems, a core challenge in logistics and transportation systems. The study proposes the first learning-to-branch framework tailored for branch-price-and-cut methods, where dynamic variables and dense constraints make traditional branching decisions computationally expensive. The novel two-stage learning-based branching (2LBB) approach effectively filters promising candidates using inexpensive features and then applies selective, partial testing to reduce costly evaluations. A theoretical model further guides dynamic adjustment of branching effort, balancing decision time with solution quality. Extensive experiments show runtime reductions of 45%–50% on standard CVRP and VRPTW benchmarks and a 47% speedup over the state-of-the-art VRPSolver when integrated into the open-source RouteOpt. These results highlight the growing potential of disciplined machine learning to enhance exact optimization algorithms.

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https://doi.org/https://doi.org/10.1287/opre.2023.0615

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@article{zhengzhong2026,
  title        = {{Two-Stage Learning to Branch in Branch-Price-and-Cut Algorithms for Solving Vehicle Routing Problems Exactly}},
  author       = {Zhengzhong You et al.},
  journal      = {Operations Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/opre.2023.0615},
}

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0.50

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F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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