论文标题
一个决策树框架，可为产品发货选择最佳盒子尺寸
A decision-tree framework to select optimal box-sizes for product shipments
论文作者
论文摘要
在包装处理设施中，使用了不同尺寸的盒子来运输产品。盒子尺寸不当的盒子比产品尺寸大得多，会造成浪费并过度增加运输成本。由于为每种产品中的每种产品制作独特的，量身定制的盒子是不可行的，因此面对电子商务公司的基本问题是：多少$ k << n $ cuboidal盒子需要制造，它们应该是什么？ In this paper, we propose a solution for the single-count shipment containing one product per box in two steps: (i) reduce it to a clustering problem in the $3$ dimensional space of length, width and height where each cluster corresponds to the group of products that will be shipped in a particular size variant, and (ii) present an efficient forward-backward decision tree based clustering method with low computational complexity on $N$ and $K$ to obtain these $K$ clusters和相应的框尺寸。我们的算法具有多个组成部分，每个部分都专门为实现高质量的聚类解决方案而设计。由于我们的方法以增量方式生成簇而不丢弃当前的解决方案，因此添加或删除大小变体就像尽早停止向后或执行一次迭代一样简单。我们通过模拟Amazon使用建议的盒子尺寸在一个月内运输的实际单算货物来测试方法的功效。即使仅修改现有的盒子尺寸并没有添加新的尺寸变体，我们也达到了$ 4.4 \％$ $ $ $ $ $ $ 2.2 \％$的减少。当我们推出$ 4 $额外的盒子时，发货量和空气量的减少显着提高到$ 10.3 \％$和$ 6.1 \％$。
In package-handling facilities, boxes of varying sizes are used to ship products. Improperly sized boxes with box dimensions much larger than the product dimensions create wastage and unduly increase the shipping costs. Since it is infeasible to make unique, tailor-made boxes for each of the $N$ products, the fundamental question that confronts e-commerce companies is: How many $K << N$ cuboidal boxes need to manufactured and what should be their dimensions? In this paper, we propose a solution for the single-count shipment containing one product per box in two steps: (i) reduce it to a clustering problem in the $3$ dimensional space of length, width and height where each cluster corresponds to the group of products that will be shipped in a particular size variant, and (ii) present an efficient forward-backward decision tree based clustering method with low computational complexity on $N$ and $K$ to obtain these $K$ clusters and corresponding box dimensions. Our algorithm has multiple constituent parts, each specifically designed to achieve a high-quality clustering solution. As our method generates clusters in an incremental fashion without discarding the present solution, adding or deleting a size variant is as simple as stopping the backward pass early or executing it for one more iteration. We tested the efficacy of our approach by simulating actual single-count shipments that were transported during a month by Amazon using the proposed box dimensions. Even by just modifying the existing box dimensions and not adding a new size variant, we achieved a reduction of $4.4\%$ in the shipment volume, contributing to the decrease in non-utilized, air volume space by $2.2\%$. The reduction in shipment volume and air volume improved significantly to $10.3\%$ and $6.1\%$ when we introduced $4$ additional boxes.
