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  4. NeuroEvolution of augmenting topologies for solving a two-stage hybrid flow shop scheduling problem
 
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2021
Journal Article
Titel

NeuroEvolution of augmenting topologies for solving a two-stage hybrid flow shop scheduling problem

Titel Supplements
A comparison of different solution strategies
Abstract
The article investigates the application of NeuroEvolution of Augmenting Topologies (NEAT) to generate and parameterize artificial neural networks (ANN) on determining allocation and sequencing decisions in a two-stage hybrid flow shop scheduling environment with family setup times. NEAT is a machine-learning and neural architecture search algorithm, which generates both, the structure and the hyper-parameters of an ANN. Our experiments show that NEAT can compete with state-of-the-art approaches in terms of solution quality and outperforms them regarding computational efficiency. The main contributions of this article are: (i) A comparison of five different strategies, evaluated with 14 different experiments, on how ANNs can be applied for solving allocation and sequencing problems in a hybrid flow shop environment, (ii) a comparison of the best identified NEAT strategy with traditional heuristic and metaheuristic approaches concerning solution quality and computational efficiency.
Author(s)
Lang, S.
Reggelin, T.
Schmidt, J.
Müller, M.
Nahhas, A.
Zeitschrift
Expert Systems with Applications
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DOI
10.1016/j.eswa.2021.114666
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Externer Link
Language
English
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Fraunhofer-Institut für Fabrikbetrieb und -automatisierung IFF
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