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制造业多级包装问题的数据驱动方法
A Data-Driven Approach for Multi-level Packing Problems in Manufacturing Industry |
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| 课程网址: |
http://videolectures.net/kdd2019_chen_tong_yuan/
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| 主讲教师: |
Lei Chen
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| 开课单位: |
华为技术有限公司
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| 开课时间: |
2020-03-02
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| 课程语种: |
英语
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| 中文简介: |
装箱问题是最基本的优化问题之一。由于其作为一个组合优化问题类的困难性及其在不同领域的广泛应用，出现了问题的不同变体，并提出了许多用于获得近似解的启发式方法。
在本文中，我们解决了一个实际的按订单制造行业场景中的多级装箱（MLBP）问题。现有解决方案不适用于该问题，因为：1.最终包装可能由多层子包装组成；2.物体的几何形状和包装约束可能未知。我们设计了一个自动包装框架，从历史记录中提取包装知识，以支持无几何形状和约束信息的包装。此外，我们提出了一种动态规划方法来寻找正常尺寸问题的最优解；以及针对大规模问题的启发式多级模糊匹配算法。使用倒排索引加快策略搜索。建议的自动包装框架已部署在华为工艺与工程系统中，以协助包装工程师。它实现了将处理5000个打包订单的执行时间加快到约8美元分钟的性能，平均成功打包率为80.54%美元，这将释放至少30%美元的打包工人工作量。
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| 课程简介: |
The bin packing problem is one of the most fundamental optimization problems. Owing to its hardness as a combinatorial optimization problem class and its wide range of applications in different domains, different variations of the problem are emerged and many heuristics have been proposed for obtaining approximate solutions.
In this paper, we solve a Multi-Level Bin Packing (MLBP) problem in the real make-to-order industry scenario. Existing solutions are not applicable to the problem due to: 1. the final packing may consist multiple levels of sub-packings; 2. the geometry shapes of objects as well as the packing constraints may be unknown. We design an automatic packing framework which extracts the packing knowledge from historical records to support packing without geometry shape and constraint information. Furthermore, we propose a dynamic programming approach to find the optimal solution for normal size problems; and a heuristic multi-level fuzzy-matching algorithm for large size problems. An inverted index is used to accelerate strategy search. The proposed auto packing framework has been deployed in Huawei Process & Engineering System to assist the packing engineers. It achieves a performance of accelerating the execution time of processing 5,000 packing orders to about $8$ minutes with an average successful packing rate as $80.54%$, which releases at least $30%$ workloads of packing workers.
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| 关 键 词: |
制造业多级包装问题; 联合机器学习; 数据驱动方法
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| 课程来源: |
视频讲座网
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| 数据采集: |
2022-09-16：cyh
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| 最后编审: |
2022-09-19：cyh
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| 阅读次数: |
50
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图 书 馆