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CUDA-Based Modified Genetic Algorithms for Solving Fuzzy Flow Shop Scheduling Problems

The flow shop scheduling problems with fuzzy processing times and fuzzy due dates are investigated in this paper. The concepts of earliness and tardiness are interpreted by using the concepts of possibility and necessity measures that were developed in fuzzy sets theory. And the objective function will be taken into account through the different combinations of possibility and necessity measures. The genetic algorithm will be invoked to tackle these objective functions. A new idea based on longest common substring will be introduced at the best-keeping step. This new algorithm reduces the number of generations needed to reach the stopping criterion. Also, we implement the algorithm on CUDA. The numerical experiments show that the performances of the CUDA program on GPU compare favorably to the traditional programs on CPU.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0823110-173701
Date23 August 2010
CreatorsHuang, Yi-chen
ContributorsZi-Cai Li, Tzon-Tzer Lu, Chien-Sen Huang, Wei-chung Wang, Tsu-Fen Chen
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0823110-173701
Rightsoff_campus_withheld, Copyright information available at source archive

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