DocumentCode :
2552275
Title :
Operation decomposition and statistical bottleneck machine identification for large-scale job shop scheduling
Author :
Zhang, Rui ; Wu, Cheng
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing
fYear :
2008
fDate :
2-4 July 2008
Firstpage :
153
Lastpage :
158
Abstract :
An decomposition-based optimization algorithm is presented for scheduling large-scale job shops with the objective of minimizing total weighted tardiness. In each iteration, we first define a new subproblem which contains a subset of operations selected from the original problem, and then we solve this newly defined subproblem using a genetic algorithm. Before each subproblem is solved, bottleneck machines are identified by a statistical method to reflect the characteristic information concerning the impending subproblem. Then, the characteristic information is used to determine the encoding scheme for the genetic algorithm. Numerical computational results show that the proposed algorithm is effective for solving large-scale scheduling problems.
Keywords :
genetic algorithms; job shop scheduling; statistical analysis; decomposition-based optimization algorithm; genetic algorithm; large-scale job shop scheduling; operation decomposition; statistical bottleneck machine identification; statistical method; Automation; Encoding; Genetic algorithms; Iterative algorithms; Job shop scheduling; Large-scale systems; Manufacturing; Processor scheduling; Scheduling algorithm; Statistical analysis; Bottleneck Machine; Decomposition; Job Shop;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-1733-9
Electronic_ISBN :
978-1-4244-1734-6
Type :
conf
DOI :
10.1109/CCDC.2008.4597289
Filename :
4597289
Link To Document :
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