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Ga based approach for optimized scheduling in a semiconductor wafer fabrication
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ACM/SIGEVO Summit on Genetic and Evolutionary Computation archive
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation table of contents
Shanghai, China
POSTER SESSION: Poster sessions table of contents
Pages 1065-1068  
Year of Publication: 2009
ISBN:978-1-60558-326-6
Authors
Zheng-Cai Cao  Beijing University of Chemical Technology, Beijing, China
Fei Qiao  Tongji University, Shanghai, China
Qi-Di Wu  Tongji University, Shanghai, China
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Scheduling for semiconductor wafer fabrication is an important problem in actual production of manufacturing system , it is also one of the difficult problems of theory research. Genetic algorithm (GA) is one major object of computational intelligence. So, the study on genetic algorithm based semiconductor wafer fabrication scheduling has been paid much attention in the past few years due to its research value and increasing amount of importance in numerous applications. In this paper, we propose a GA based scheduler. In the GA scheduler, the chromosome represents a combination of scheduling policies, including lot released policies, machine selection rules, dispatch rules and batch rules. So, when the GA finishes its optimization process, an optimal scheduling policy is produced. From our experiment results, the GA will yield a more efficient solution than several other scheduler.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
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Collaborative Colleagues:
Zheng-Cai Cao: colleagues
Fei Qiao: colleagues
Qi-Di Wu: colleagues