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Solving fuzzy flexible job shop scheduling problems using genetic algorithm

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2 Author(s)
De-Ming Lei ; Sch. of Autom., Wuhan Univ. of Technol., Wuhan ; Xiu-Ping Guo

This paper presents a two-population genetic algorithm (TPGA) for FfJSSPs with the maximum fuzzy completion time. TPGA uses two-string representation to represent a solution and two populations to search the optimal schedule. In each generation, crossover and mutation are only applied to one part of the chromosome and these populations are combined and updated by using half of the individuals with the bigger fitness in the combined population. Some instances of FfJSSP are designed and the performance of TPGA is tested. The computational results demonstrate the promising performance of TPGA on FfJSSP.

Published in:

Machine Learning and Cybernetics, 2008 International Conference on  (Volume:2 )

Date of Conference:

12-15 July 2008