A partial-clustering based algorithm for iterative partial-decomposition of large scale job shops scheduling problems

For large scale scheduling problems, the iterative decomposition is a feasible approach to reduce the size of problems. A partial clustering based algorithm is proposed for the iterative partial decomposition of large-scale scheduling problems in this paper. The efficiency of the new method is illus...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 4; pp. 2275 - 2278
Main Authors Yun-Dong Gu, De-Gang Chen, Guo-Dong Li, Min Liu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Clustering algorithms
Cybernetics
Iterative algorithms
Job shop scheduling
Large scale scheduling
Large-scale systems
Machine learning
Manufacturing
Partial clustering
Partial decomposition
Processor scheduling
Production
Scheduling algorithm
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Summary:For large scale scheduling problems, the iterative decomposition is a feasible approach to reduce the size of problems. A partial clustering based algorithm is proposed for the iterative partial decomposition of large-scale scheduling problems in this paper. The efficiency of the new method is illustrated by numerical computational results on several large-scale production scheduling problems.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212142