Operation decomposition and statistical bottleneck machine identification for large-scale job shop scheduling

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 t...

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Vydáno v:2008 Chinese Control and Decision Conference s. 153 - 158
Hlavní autoři: Rui Zhang, Cheng Wu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2008
Témata:
Bottleneck Machine
Decomposition
Encoding
Genetic algorithms
Job Shop
Job shop scheduling
Optimization
Schedules
Scheduling
Search problems
ISBN:9781424417339, 1424417333
ISSN:1948-9439
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Shrnutí: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.
ISBN:9781424417339
1424417333
ISSN:1948-9439
DOI:10.1109/CCDC.2008.4597289