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Learning-Based Optimization for Vehicle/Robot Routing Problems: A Survey

  • Jun Li

Received: 19 Jan 2026 | Revised: 22 May 2026 | Accepted: 25 May 2026 | Published: 11 Jun 2026

Abstract

Learning-based Neural Combinatorial Optimization (NCO) is an emerging paradigm for various vehicle/robot routing problems. It transitions solution strategies from manual heuristics to data-driven learning. This paper presents a systematic survey of deep learning-based approaches for route optimization. We first unify classical routing models and formulate reinforcement learning methods within a Markov Decision Process (MDP) framework. Existing literature is primarily classified into two categories: (1) end-to-end neural solvers, encompassing constructive and improvement-based methods, constraint-handling techniques, and various encoder–decoder or generative training schemes; and (2) scalability-oriented solvers, which leverage divide-and-conquer strategies to address large-scale routing problems. Finally, we discuss vital future research directions, including the integration of heuristic knowledge into NCO, large-scale multi-objective optimization, and automated modeling/solving. This survey offers a structured taxonomy of learning-based route optimization methods and discusses the potential for extending them to a broader class of combinatorial optimization problems.

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10.53941/jaia.2026.1000011
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Volume 1, Issue 2
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Li, J. Learning-Based Optimization for Vehicle/Robot Routing Problems: A Survey. Journal of Artificial Intelligence for Automation 2026, 1 (2), 11. https://doi.org/10.53941/jaia.2026.1000011.
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