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A Neural Network Guided Genetic Algorithm for Flexible Flow Shop Scheduling Problem with Sequence Dependent Setup Time

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dc.contributor.advisor Defersha, Fantahun
dc.contributor.author Tahsien, Syeda Manjia
dc.date.accessioned 2020-12-17T20:12:34Z
dc.date.available 2020-12-17T20:12:34Z
dc.date.copyright 2020-12
dc.date.created 2020-12-02
dc.date.issued 2020-11-14
dc.identifier.uri https://hdl.handle.net/10214/23664
dc.description.abstract This thesis presents a discriminating technique and clustering ordered permutation using Adaptive Resonance Theory (ART) and potential applications in the ART-guided Genetic Algorithm (GA). In this regard, we have introduced two novel techniques for converting ordered permutations to binary vectors to cluster them using ART. The proposed binary conversion methods are evaluated under varying parameters, and problem sizes with the performance analysis of ART-1 and Improved-ART-1. The numerical results indicate the superiority of one of the proposed binary conversion techniques over the other and Improved-ART-1 over ART-1. Finally, we develop Improved-ART-1 Neural Network guided GA to solve a flexible flow show scheduling problem (FFSP) with sequence-dependent setup time. Numerical examples show that ANN-guided GA outperforms the pure GA in solving several large size FFSP problems. en_US
dc.language.iso en en_US
dc.publisher lSCMl20 Conference Proceeding en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Adaptive Resonance Theory en_US
dc.subject ANN-Guided Genetic Algorithm en_US
dc.subject Binary Conversion Method en_US
dc.subject Flexible Flow Shop en_US
dc.subject Genetic Algorithm en_US
dc.subject Neural Network en_US
dc.subject Ordered Permutations en_US
dc.title A Neural Network Guided Genetic Algorithm for Flexible Flow Shop Scheduling Problem with Sequence Dependent Setup Time en_US
dc.type Thesis en_US
dc.degree.programme Engineering en_US
dc.degree.name Master of Applied Science en_US
dc.degree.department School of Engineering en_US
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