Evolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times

dc.contributor.author Lang, Sebastian
dc.contributor.author Reggelin, Tobias
dc.contributor.author Behrendt , Fabian
dc.contributor.author Nahhas, Abdulrahman
dc.date.accessioned 2020-01-04T07:24:52Z
dc.date.available 2020-01-04T07:24:52Z
dc.date.issued 2020-01-07
dc.description.abstract We present a novel strategy to solve a two-stage hybrid flow shop scheduling problem with family setup times. The problem is derived from an industrial case. Our strategy involves the application of NeuroEvolution of Augmenting Topologies - a genetic algorithm, which generates arbitrary neural networks being able to estimate job sequences. The algorithm is coupled with a discrete-event simulation model, which evaluates different network configurations and provides training signals. We compare the performance and computational efficiency of the proposed concept with other solution approaches. Our investigations indicate that NeuroEvolution of Augmenting Topologies can possibly compete with state-of-the-art approaches in terms of solution quality and outperform them in terms of computational efficiency.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.160
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63899
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Intelligent Decision Support and Big Data for Logistics and Supply Chain Management
dc.subject neuroevolution
dc.subject scheduling
dc.subject real-time
dc.subject artificial intelligence
dc.title Evolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times
dc.type Conference Paper
dc.type.dcmi Text
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