A greedy online 3D bin packing algorithm based on multi-indicator fusion

Lixin Ma, Wei Wang, Tong Zhang, Xincheng Tian, Yong Jiang
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Abstract

In this paper, we focus on the online 3D bin packing problem, a classical strong NP-hard problem. In the problem, each item is unknown before bin packing is performed, and the arrival of the item requires immediate bin packing, which has many applications in industrial automation. In this paper, we propose a greedy algorithm for multi-indicator fusion to solve this problem by defining a series of evaluation indicators during bin packing, determining the weights of these indicators to be fused by SVR algorithm and Quasi-Newton Methods, and finally selecting the placement with the highest score of the fused indicators to be placed. The experimental results show that this method can solve the online 3D bin packing problem and is competitive with other algorithms in terms of space utilization and the number of bins, and the running time is fully completed to meet the online bin packing requirements.
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基于多指标融合的贪婪在线 3D 仓包装算法
在本文中,我们将重点研究在线 3D 仓储包装问题,这是一个经典的强 NP 难问题。在该问题中,每个物品在装箱前都是未知的,物品到达后需要立即装箱,这在工业自动化领域有很多应用。本文提出了一种多指标融合的贪婪算法来解决这一问题,即在料仓打包过程中定义一系列评价指标,通过 SVR 算法和准牛顿方法确定这些指标的权重进行融合,最后选择融合后指标得分最高的放置点进行放置。实验结果表明,该方法可以解决在线三维料仓打包问题,在空间利用率和料仓数量方面与其他算法相比具有竞争力,运行时间完全可以满足在线料仓打包的要求。
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