標題: Attribute selection for neural network-based adaptive scheduling systems in flexible manufacturing systems
作者: Shiue, YR
Su, CT
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: adaptive scheduling;attribute selection;flexible manufacturing systems;knowledge based systems;learning by example;neural network
公開日期: 2002
摘要: System attribute selection is an integral part of adaptive scheduling systems. Owing to the existence of irrelevant and redundant attributes in manufacturing systems, by selecting the important attributes, better performance or accuracy in prediction can be expected in scheduling knowledge bases. In this study, we first propose an attribute selection algorithm based on the weights of neural networks to measure the importance Of system attributes in a neural network-based adaptive scheduling (NNAS) system. Next, the NNAS system is combined with the attribute selection algorithm to build scheduling knowledge bases. This hybrid approach is called an attribute selection neural network-based adaptive scheduling (ASNNAS) system. The experimental results show that the proposed ASNNAS system works very well, when measured by a variety of performance criteria, as opposed to the traditional NNAS system and a single dispatching strategy. Furthermore, the scheduling knowledge bases in the ASNNAS system can provide a stronger generalisation ability compared with NNAS systems under various performance criteria.
URI: http://hdl.handle.net/11536/29112
http://dx.doi.org/10.1007/s001700200187
ISSN: 0268-3768
DOI: 10.1007/s001700200187
期刊: INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume: 20
Issue: 7
起始頁: 532
結束頁: 544
Appears in Collections:Articles


Files in This Item:

  1. 000179157300008.pdf