.png){kind=icon}
返回
A machine learning based EMA-DCPM algorithm for production scheduling
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-024-71355-w.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Some special manufacturing fields such as aerospace may encounter mixed production of multiple research and development projects and multiple batch production projects. Under these special production conditions resource conflicts are more severe, resulting in uncertain operating times that are difficult to predict. In addition, a single project may have tens of thousands of supporting products, making it difficult to effectively control the total construction process. To address these challenges this paper proposes new methods. A model, EMA-DCPM (dynamic critical path method) incorporating attention mechanisms in Enterprise Resource Planning and Mechanical Engineering Society) has been proposed. This model predicts product job time through machine learning methods and discovers the predictive advantage of the attention mechanism through data comparison. The CPM control algorithm was improved to enhance its robustness and an efficient modeling method, 5+X was proposed. This new method is suitable for mixed line planning management in sophisticated manufacturing projects and has value for practical applications.
Keyword:
Mixed production lines
Multiple varieties
Variable batches
EMA-DCPM
Production scheduling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
28.0W
被引数:
83.5W
机构
引用论文
Investigation of green production inventory problem with selling price and green level sensitive interval-valued demand via different metaheuristic algorithms.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems
PREAI
分享收藏分享收藏Serum Phosphate as a Risk Factor for Cardiovascular Events in People with and without Chronic Kidney Disease: A Large Community Based Cohort StudyPLoS ONEOAAI
PLoS ONEIF0
OAAI分享收藏
查看更多内容