DocumentCode :
466964
Title :
An algorithm of job shop rolling scheduling based on singular rough sets
Author :
Hu, Yongmei ; Fu, Yanan ; Jia, Lei ; Li, Qiqiang
Author_Institution :
Shandong Univ., Jinan
Volume :
2
fYear :
2007
fDate :
July 30 2007-Aug. 1 2007
Firstpage :
221
Lastpage :
225
Abstract :
A new kind of rolling scheduling algorithm based on singular rough sets is put forward in the article. Under dynamic processing environment, a job identification problem in a rolling horizon optimal scheduling window is investigated. Using a new artificial intelligence method, singular rough sets, this article presents a new job identification algorithm of job shop rolling scheduling. It bases on the dynamic transfer characteristic and the assistant set of singular rough sets. In case of the machine failure, the due date of the jobs changed and the urgent job, the re-selection and re-scheduling of the jobs in the rolling window are executed once more. Solved by genetic algorithm, the illustration example verifies the effectively of the algorithm, not only the dynamic processing environment is adapted and a satisfied rescheduling result is obtained, but also the re-scheduling dimension is decreased.
Keywords :
dynamic scheduling; genetic algorithms; job shop scheduling; rough set theory; artificial intelligence; dynamic processing; dynamic transfer characteristic; genetic algorithm; job identification; job shop rolling scheduling; rescheduling; singular rough set; Artificial intelligence; Distributed computing; Dynamic scheduling; Genetic algorithms; Job shop scheduling; Optimal scheduling; Rough sets; Scheduling algorithm; Software algorithms; Software engineering; Job; Rolling scheduling; Rough sets; Scheduling algorithm; Singular rough sets; shop scheduling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-0-7695-2909-7
Type :
conf
DOI :
10.1109/SNPD.2007.144
Filename :
4287682
Link To Document :
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