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A Neural Column Generation Approach to the Vehicle Routing Problem with Two-Dimensional Loading and Last-In-First-Out Constraints

arXiv 2406.12454 ↗First posted 18 Jun 2024cs.AIcited by 0

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Abstract (arXiv)

The vehicle routing problem with two-dimensional loading constraints (2L-CVRP) and the last-in-first-out (LIFO) rule presents significant practical and algorithmic challenges. While numerous heuristic approaches have been proposed to address its complexity, stemming from two NP-hard problems: the vehicle routing problem (VRP) and the two-dimensional bin packing problem (2D-BPP), less attention has been paid to developing exact algorithms. Bridging this gap, this article presents an exact algorithm that integrates advanced machine learning techniques, specifically a novel combination of attention and recurrence mechanisms. This integration accelerates the state-of-the-art exact algorithm by a median of 29.79% across various problem instances. Moreover, the proposed algorithm successfully resolves an open instance in the standard test-bed, demonstrating significant improvements brought about by the incorporation of machine learning models. Code is available at https://github.com/xyfffff/NCG-for-2L-CVRP.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 39 references that are in the map. First posted on arXiv 18 Jun 2024.

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14 of 39 references.

References · 14

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2 more
  1. 2021Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Surveycited by 3
  2. 2023A Reinforcement-Learning-Based Multiple-Column Selection Strategy for Column Generationcited by 3

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