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
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;
Conference_Titel :
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN :
0-7803-7508-4
DOI :
10.1109/ICMLC.2002.1167501
