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Learning to Solve Vehicle Routing Problems: A Survey
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This paper provides a systematic overview of machine learning methods applied to solve NP-hard Vehicle Routing Problems (VRPs). Recently, there has been a great interest from both machine learning and operations research communities to solve VRPs either by pure learning methods or by combining them with the traditional hand-crafted heuristics. We present the taxonomy of the studies for learning paradigms, solution structures, underlying models, and algorithms. We present in detail the results of the state-of-the-art methods demonstrating their competitiveness with the traditional methods. The paper outlines the future research directions to incorporate learning-based solutions to overcome the challenges of modern transportation systems.
Forward citations
Cited by 3 Pith papers
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Neural Embedded Mixed-Integer Optimization for Location-Routing Problems
A neural cost predictor embedded in a mixed-integer program chooses depots and customer assignments for location-routing, reaching near best-known solutions on large benchmarks with seconds of allocation time.
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A Coalition Game for On-demand Multi-modal 3D Automated Delivery System
Proposes a graph-attention deep RL planner for UAV-ADR last-mile pickup and delivery with time windows, plus a Shapley-value coalition analysis of cooperation benefits.
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CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention
CaDA combines a constraint prompt and dual global/sparse attention to beat prior neural cross-problem solvers on 16 VRP variants, though traditional heuristic solvers still yield lower average gaps.
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