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A hybrid neural network- meta heuristics approach for permutation flow shop scheduling problems
Published in IEEE
The objective of this study is to find a sequence of jobs for the permutation flow shop to minimize makespan. A feed forward back propagation neural network is used to solve the 10 machine problem taken from the literature. The network is trained with the optimal sequences for five, six and seven jobs problem. This trained network is then used to solve the problem with greater number of jobs. The sequence obtained using neural network is used to generate initial population for genetic algorithm (ANN-GA), genetic algorithm using Random Insertion Perturbation Scheme (ANN-GA-RIPS) and Simulated Annealing (ANN-SA). Makespans obtained through these approaches are compared with the Taillard's benchmark problems. extcopyright2010 IEEE.
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JournalData powered by Typeset2010 IEEE International Conference on Industrial Engineering and Engineering Management
PublisherData powered by TypesetIEEE
Open Access0