Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22314
Title: Genetic synthesis of production-control systems for unreliable manufacturing systems with variable demands
Authors: Mok, PY 
Keywords: Gain-scheduled adaptive control
Genetic algorithms
Robust fuzzy control
Unreliable manufacturing systems
Variable demands
Issue Date: 2011
Publisher: Pergamon Press
Source: Computers and industrial engineering, 2011, v. 61, no. 1, p. 198-208 How to cite?
Journal: Computers and industrial engineering 
Abstract: The control of manufacturing systems with variable demands has attracted much research attention over the years. However, only limited results have been obtained due to the difficulty of this production-control problem. In this paper, genetically optimized short-run hedging points are used to construct gain-scheduled adaptive controllers for unreliable manufacturing systems with variable demands. The performance of such adaptive controllers is illustrated for unreliable systems subjected to piecewise-constant demands. It is demonstrated that the performance of these adaptive controllers is superior, in general, to that of genetically optimized non-adaptive controllers. However, such gain-scheduled adaptive controllers are designed for variable demands that are piecewise-constant. Therefore, in order to deal with more general classes of variable demands, a genetic rule-induction design methodology is used to synthesize robust fuzzy-logic controllers to provide automatic closed-loop control for unreliable manufacturing systems. Such robust fuzzy-logic controllers are shown to provide effective control for unreliable manufacturing systems with various kinds of variable demands.
URI: http://hdl.handle.net/10397/22314
ISSN: 0360-8352
EISSN: 1879-0550
DOI: 10.1016/j.cie.2011.03.010
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