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<p>The Distributor’s Pallet Loading Problem aims to optimize the loading of different 3D boxes on the minimum number of pallets. We consider an Integer Linear Programming (ILP) model for the problem that includes constraints deriving from real applications, such as stability and compression limits. In order to solve the ILP problem efficiently, we propose a method that exploits Machine Learning algorithms to classify predetermined layers of boxes, based on their “importance” of being used for an ILP solution. This classification is used to heuristically limit the number of layers taken into account by the ILP solver. We demonstrate the effectiveness of our approach by comparing the ILP solution with and without the Machine Learning component. The numerical results show that the proposed Machine Learning matheuristic approach achieves optimized pallet loading solutions in significantly reduced computational time.</p>

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Can machine learning help in solving the pallet loading optimization problem?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月24日

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  • Mauro Dell’Amico,
  • Giorgia Franchini,
  • Matteo Magnani,
  • Luca Zanni

摘要

The Distributor’s Pallet Loading Problem aims to optimize the loading of different 3D boxes on the minimum number of pallets. We consider an Integer Linear Programming (ILP) model for the problem that includes constraints deriving from real applications, such as stability and compression limits. In order to solve the ILP problem efficiently, we propose a method that exploits Machine Learning algorithms to classify predetermined layers of boxes, based on their “importance” of being used for an ILP solution. This classification is used to heuristically limit the number of layers taken into account by the ILP solver. We demonstrate the effectiveness of our approach by comparing the ILP solution with and without the Machine Learning component. The numerical results show that the proposed Machine Learning matheuristic approach achieves optimized pallet loading solutions in significantly reduced computational time.