Title :
A partial-clustering based algorithm for iterative partial-decomposition of large scale job shops scheduling problems
Author :
Gu, Yun-dong ; Chen, De-Gang ; Li, Guo-dong ; Liu, Min
Author_Institution :
Sch. of Math. & Phys., North China Electr. Power Univ., Beijing, China
Abstract :
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.
Keywords :
iterative methods; job shop scheduling; optimisation; pattern clustering; resource allocation; iterative partial-decomposition approach; large-scale job shop scheduling problem; large-scale production scheduling problem; numerical computation; partial clustering-based algorithm; resource optimization problem; Clustering algorithms; Cybernetics; Iterative algorithms; Job shop scheduling; Large-scale systems; Machine learning; Manufacturing; Processor scheduling; Production; Scheduling algorithm; Large scale scheduling; Partial clustering; Partial decomposition;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212142
