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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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Title: A Neural Network Guided Genetic Algorithm for Flexible Flow Shop Scheduling Problem with Sequence Dependent Setup Time
Author: Tahsien, Syeda Manjia
Department: School of Engineering
Program: Engineering
Advisor: Defersha, Fantahun
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.
URI: https://hdl.handle.net/10214/23664
Date: 2020-12
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International


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Attribution-NonCommercial-NoDerivatives 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International