作者
Paolo Priore, David De La Fuente, Alberto Gomez, Javier Puente
发表日期
2001/6
来源
Ai Edam
卷号
15
期号
3
页码范围
251-263
出版商
Cambridge University Press
简介
A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no single rule exists that is better than the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a review of the main machine learning-based scheduling approaches described in the literature is presented.
引用总数
学术搜索中的文章
P Priore, D De La Fuente, A Gomez, J Puente - Ai Edam, 2001