DocumentCode :
478960
Title :
Genetic Algorithm-Based Rules Discovery for Networked Manufacturing Resources Management
Author :
Wang, Bin ; Zhou, Ning ; Liu, Defang ; Zhou, Linzhen ; Wang, Ping
Author_Institution :
UGS Sch., Yancheng Inst. of Technol., Yancheng
fYear :
2008
fDate :
12-14 Oct. 2008
Firstpage :
1
Lastpage :
4
Abstract :
To choose and manage networked manufacturing resources efficiently, a fuzzy association rules mining technique was proposed. According to the analysis of all data types, a specialized data preprocessing method was applied to extract, wash and transform networked manufacturing resources data, and a self-contained data warehouse on networked manufacturing resources was established for data preprocessing. To deal with the fuzzy association rules mining on manufacturing resources platform, a novel genetic algorithm (GA) based mining method was introduced. Considering the characteristics of networked manufacturing resources, double-level encoding and label-bit switching operator were designed to make the improved GA available to networked manufacturing resources. Finally, a case was employed to demonstrate the practicality of applying fuzzy association rules mining based on GA in networked manufacturing resources management.
Keywords :
data mining; data warehouses; fuzzy set theory; genetic algorithms; manufacturing data processing; data preprocessing method; double-level encoding operator; fuzzy association rule mining technique; genetic algorithm-based rules discovery; label-bit switching operator; networked manufacturing resource management; self-contained data warehouse; Algorithm design and analysis; Association rules; Data mining; Data preprocessing; Data warehouses; Databases; Genetic algorithms; Manufacturing processes; Quality management; Resource management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
Type :
conf
DOI :
10.1109/WiCom.2008.2508
Filename :
4680697
Link To Document :
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