.png){kind=icon}
返回
Learning effective new single machine dispatching rules from optimal scheduling data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijpe.2010.06.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The expertise of the scheduler plays an important role in creating production schedules, and the schedules created in the past thus provide important information about how they should be done in the future. Motivated by this observation, we learn new scheduling rules from existing schedules using data mining techniques. However, direct data mining of scheduling data can at best mimic existing scheduling practices. We therefore propose a novel two-phase approach for learning, where we first learn which part of the data correspond to best scheduling practices and then use this data and decision tree induction to learn new and previously unknown dispatching rules. Our numerical results indicate that the newly learned rules can be a significant improvement upon the underlying scheduling rules, thus going beyond mimicking existing practice. (C) 2010 Elsevier B.V. All rights reserved.
Keyword:
Scheduling
Dispatching rules
Data mining
Classification
Decision trees
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:
10
论文数:
8.1K
被引数:
3.6W
机构
引用论文
Effectiveness and cost-effectiveness of a tailored text-message programme (MiQuit) for smoking cessation in pregnancy: study protocol for a randomised controlled trial (RCT) and meta-analysis.png)
Trials
OAAI
TrialsIF0
OAAI分享收藏Rate‐responsive pacemakers and anaesthesia A consideration of possible implicationsAnaesthesia
AnaesthesiaIF0
PREAI
分享收藏
查看更多内容