arXiv AI
VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
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arXiv AI
Jun 18
Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design
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.
arXiv AI
Sep 2
Reinforcement Learning Enhanced LLM Agents for Complex Vehicle Routing Problems
The paper introduces Reinforcement Learning Enhanced LLM Agents (RLEA), a multi‑agent framework that automates the modeling of complex Vehicle Routing Problems (VRPs). RLEA employs a lightweight neural Planner trained with Soft Q‑learning to coordinate LLM‑based agents, and incorporates an evolutionary memory module and retrieval‑augmented generation to leverage experience and external solver knowledge. Experiments on 48 VRP variants show that RLEA outperforms the prior state‑of‑the‑art method, achieving a 16.67% higher success rate and significantly reducing runtime errors.
arXiv Machine Learning
Aug 17
Smart routes: a system for development and comparison of algorithms for solving vehicle routing problems with realistic constraints
arXiv:2608. 14140v1 Announce Type: new Abstract: The problem of route optimization with realistic constraints is becoming extremely relevant in the face of global urban population growth.
arXiv AI
Jun 2
Learning to Reduce Search Space for Generalizable Neural Routing Solver
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.
arXiv AI
Jun 18
LLM-Evolved Domain-Independent Heuristics for Symbolic AI Planning
arXiv:2605. 29649v2 Announce Type: replace Abstract: Heuristic search is the dominant paradigm in symbolic AI planning, and the strongest heuristics are the result of decades of work by planning researchers.
arXiv AI
Oct 16
Self-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems
The paper introduces a self‑guided reasoning framework that enables large language models (LLMs) to detect and correct errors in Vehicle Routing Problem (VRP) instances. By autonomously generating code that offers structured insights, the approach addresses limitations of existing methods, especially for complex instances or errors that increase reasoning load. Experiments across various problem settings and LLMs show that this framework markedly improves error detection, achieving near‑perfect accuracy.