Vehicle Routing on Ricky Zhengzhong You

RouteOpt

Sat, 04 Apr 2026 00:00:00 +0000

A public modular exact solver for vehicle routing problems.

RouteOpt: An Open-Source Modular Exact Solver for Vehicle Routing Problems

Sat, 04 Apr 2026 00:00:00 +0000

Article accepted by INFORMS Journal on Computing.

DOI: 10.1287/ijoc.2025.1415
Preprint: PDF
Repository: GitHub
Documentation: RouteOpt Docs
Replication archive: INFORMSJoC/2025.1415

Despite significant advancements in exact methods for vehicle routing problems (VRPs) over the past three decades, there remains a lack of high-performing and accessible open-source solvers for researchers and practitioners. To bridge this gap, we introduce RouteOpt, the first open-source modular exact solver for VRPs, delivering state-of-the-art performance while maintaining a flexible and extensible structure. RouteOpt achieves the best performance reported in the literature on both the capacitated vehicle routing problem (CVRP) and vehicle routing problem with time windows (VRPTW). Crucially, its modular design allows users to develop and integrate customized branching, cutting plane, and variable reduction modules to tackle a broad range of VRP variants. Furthermore, RouteOpt introduces a novel node restoration mechanism, enabling efficient parallel processing of a branch-and-bound tree. Leveraging this feature, we have, for the first time, proven the optimality of three open CVRP instances. By combining modularity, efficiency, and open accessibility, RouteOpt establishes itself as an invaluable platform for both academic research and real-world applications.

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

Tue, 24 Feb 2026 00:00:00 +0000

Published in Operations Research.

DOI: 10.1287/opre.2023.0615
Preprint: PDF
Replication archive: ORJournal/2023.0615

Branching is one of the most important components in branch-price-and-cut (BPC) algorithms for solving vehicle routing problems (VRPs) exactly. However, learning to branch is much more challenging in BPC than in branch-and-cut algorithms that are used for solving general mixed integer programs because branching is generally performed by adding a dense constraint to the restricted master problem, while the variables in the restricted master problem change constantly. To address such challenges, we propose the first effective learning-to-branch framework in BPC algorithms, leading to a novel two-stage learning-based branching (2LBB) strategy. This serves as an innovative learning-based enhancement for the cutting-edge three-phase branching strategy for column-generation-based algorithms. In the 2LBB, the first stage focuses on narrowing down the list of promising candidates using computationally cheap features, thereby lessening dependence on LP testing. The second stage, meanwhile, diminishes the burden on heuristic testing through an innovative partial testing approach. Moreover, we propose a novel theoretical model characterizing the fundamental trade-off between time spent making a single branching decision and the resulting branching quality. A formula derived from the model for dynamically adjusting the number of candidates to select for the second stage achieves consistently superior performance to ones obtained from trial-and-error tuning. Through an extensive numerical study, we demonstrate that a dynamic version of the 2LBB, denoted by 2LBB-dy, achieves approximately 45% and 50% time reduction, respectively, compared to the state-of-the-art hand-crafted branching strategy in solving the capacitated vehicle routing problem (CVRP) and vehicle routing problem with time windows (VRPTW). In addition, RouteOpt, when equipped with the 2LBB-dy, achieves a 47% time reduction compared to the state-of-the-art VRPSolver for the CVRP.

Fairness in Capacitated Vehicle Routing Problem

Wed, 01 Oct 2025 00:00:00 +0000

Stay tuned.

RouteOpt: A Scalable Advanced Optimization Tool for VRPs

Tue, 01 Oct 2024 00:00:00 +0000

Presented at the 2024 INFORMS Annual Meeting on modular exact solver design for vehicle routing problems.