DocumentCode
387608
Title
A genetic learning approach with case-based memory for job-shop scheduling problems
Author
Yin, Wen-Jun ; Liu, Min ; Wu, Cheng
Author_Institution
Dept. of Autom., Tsinghua Univ., Beijing, China
Volume
3
fYear
2002
fDate
2002
Firstpage
1683
Abstract
A new genetic learning approach for job-shop scheduling problems (JSP) is proposed inspired by case-based reasoning (CBR). Firstly, based on DNA matching ideas, job similarity and problem solution similarity are defined respectively. The case retrieval and adaptation methods focusing on preserving and reusing useful building blocks are then studied in detail. An integrated CBR-GA framework is thoroughly researched and tested in JSP environments and adaptive schedules are obtained.
Keywords
case-based reasoning; genetic algorithms; learning (artificial intelligence); production control; CBR; DNA matching; JSP; case adaptation methods; case retrieval; case-based memory; case-based reasoning; genetic learning; job similarity; job-shop scheduling problems; problem solution similarity; Adaptive scheduling; Automation; DNA; Genetic algorithms; Learning systems; Machine learning; Optimization methods; Routing; Scheduling algorithm; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN
0-7803-7508-4
Type
conf
DOI
10.1109/ICMLC.2002.1167501
Filename
1167501
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