Title page for etd-0823110-173701


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URN etd-0823110-173701
Author Yi-chen Huang
Author's Email Address No Public.
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Department Applied Mathematics
Year 2009
Semester 2
Degree Master
Type of Document
Language English
Title CUDA-Based Modified Genetic Algorithms for Solving Fuzzy Flow Shop Scheduling Problems
Date of Defense 2010-06-10
Page Count 32
Keyword
  • CUDA framework
  • Fuzzy number
  • Genetic algorithm
  • Flow shop scheduling problem
  • Abstract 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.
    Advisory Committee
  • Tzon-Tzer Lu - chair
  • Zi-Cai Li - co-chair
  • Wei-chung Wang - co-chair
  • Tsu-Fen Chen - co-chair
  • Chien-Sen Huang - advisor
  • Files
  • etd-0823110-173701.pdf
  • indicate in-campus access immediately and off_campus access in a year
    Date of Submission 2010-08-23

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