3-1 运输箱设计的机器学习方法.pdf

1、A Machine Learning Approach to Shipping Box DesignGuang Yang,Cun(Matthew)MuJ To minimize the overall shipping cost by strategically selecting the best combination of k box sizes from thousands of feasible ones to be responsible for hundreds of thousands of orders daily placed on millions of inventor

2、y products.Overall lowest shipping costPack use k=18 box sizesselect from thousands of feasible box sizesXXXOverview Key:view each box size as a point in space and formulate the box design problem as a generalized version of weighted k-medoids clustering problem to recommend k=18 box sizes.Q:How to

3、define and measure the weight Q:How to define and measure the weight w wj jand distance and distance c cij ijfor each box size?for each box size?Box:10”x8”x7”Box:9”x8”x8”Box:22”x5”x3”Note that it is possible that c cij ij c cji ji,and often this is the case.Workflow Generate all candidate box sizes.

4、Pack customer order into full box set,often thousands of box types.To determine the box economic value,e.g.,weight wj.Evaluate how easily box Bican replace another box Bj,when an order is best packed(highest utilization rate)by Bj.Define box-box economic substitution cost cij.Solve the generalized w

5、eighted k-medoids clustering problem given wjand cij.Parameter tuning and result evaluation.1.Generate all candidate box sizes Requirements:This yields 3,391 different types of boxesBox IDLength(in)Depth(in)Height(in)Volume(in3)Weight Limit(oz)B0001853120800B0002953135800800B339136211410584800volume

6、=10800(due to handling capability)box size volume(in3)shipping rates($)2.Pack customer orders into full box set.Pack 3-months historical orders using all 3391 boxes.We developed a super efficient packing algorithm called gbp that can solve this 4D packing within 1%suboptimality,but 100 x or even mor