DECISION LEARNING ABOUT PRODUCTION CONTROL AS MACHINES BREAK DOWN IN A FLEXIBLE MANUFACTURING SYSTEM

NCUIR > Engineering College > Graduate Institute of Mechanical Engineering > journal & Dissertation > Item 987654321/27062

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27062

Title:	DECISION LEARNING ABOUT PRODUCTION CONTROL AS MACHINES BREAK DOWN IN A FLEXIBLE MANUFACTURING SYSTEM
Authors:	WANG,KS;HSIA,HW;ZHUANG,ZD
Contributors:	機械工程學系
Keywords:	N/A
Date:	1995
Issue Date:	2010-06-29 18:06:46 (UTC+8)
Publisher:	中央大學
Abstract:	During manufacturing, there are many situations that can affect production performance. Such situations include machine breakdowns, rush orders, order changes, and order delays. When such issues occur, one has to make decisions to try to maintain production efficiency. Human decisions tend to be too late and incomplete in such contingencies. Thus a system that can make better decisions in time to maintain production performance is needed. To achieve this objective, the intelligent decision system described in this paper integrates artificial intelligence, an optimization technique, and simulation to serve such problems. The decision-making logic of the intelligent decision system is described by event graphs. It imitates the manner of human thinking. Self-learning of the decision-making process is used to strengthen the decision quality. In this study, a method of rule induction is applied to build up the self-learning system. There are two subsystems included in this system. One is rule generation and the other is knowledge management. A case for machine breakdowns is presented and discussed. A series of tests designed to validate the self-learning system are presented. These demonstrate that a rule induction method is suitable for constructing the self-learning.
Relation:	INTERNATIONAL JOURNAL OF FLEXIBLE MANUFACTURING SYSTEMS
Appears in Collections:	[Graduate Institute of Mechanical Engineering] journal & Dissertation

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