Dynamic production bottleneck prediction using a data-driven method in discrete manufacturing system-Reference-Cited by-同舟云学术

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Dynamic production bottleneck prediction using a data-driven method in discrete manufacturing system

Published:2023-10 Issue: Volume:58 Page:102162
ISSN:1474-0346
Container-title:Advanced Engineering Informatics
language:en
Short-container-title:Advanced Engineering Informatics

Author:

Liu Daoyuan,Guo Yu,Huang Shaohua,Wang Shengbo,Wu Tao

Funder

National Natural Science Foundation of China

Science and Technology Support Program of Jiangsu Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems,Building and Construction

Reference44 articles.

1. Introduction of a time series machine learning methodology for the application in a production system;Hennig;Adv. Eng. Inf.,2021

2. A two-stage transfer learning-based deep learning approach for production progress prediction in IoT-enabled manufacturing;Huang;IEEE Internet Things J.,2019

3. Predicting bottlenecks in manufacturing shops through capacity and demand observations from multiple perspectives;Tang;Int. J. Technol. Manag.,2018

4. Detecting bottlenecks in serial production lines–a focus on interdeparture time variance;Betterton;Int. J. Prod. Res.,2018

5. A systematic-theoretic analysis of data-driven throughput bottleneck detection of production systems;Li;J. Manuf. Syst.,2018

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. E-YQP: A self-adaptive end-to-end framework for quality prediction in yarn spinning manufacturing;Advanced Engineering Informatics;2024-10

2. A Comprehensive Review of Theories, Methods, and Techniques for Bottleneck Identification and Management in Manufacturing Systems;Applied Sciences;2024-08-31

3. Research on dynamic bottleneck prediction methods in manufacturing workshops;Journal of Physics: Conference Series;2024-08-01

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