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

Simple item page
dc.contributor.authorLang, Sebastian
dc.contributor.authorReggelin, Tobias
dc.contributor.authorBehrendt , Fabian
dc.contributor.authorNahhas, Abdulrahman
dc.date.accessioned2020-01-04T07:24:52Z
dc.date.available2020-01-04T07:24:52Z
dc.date.issued2020-01-07
dc.description.abstractWe 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.160
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63899
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIntelligent Decision Support and Big Data for Logistics and Supply Chain Management
dc.subjectneuroevolution
dc.subjectscheduling
dc.subjectreal-time
dc.subjectartificial intelligence
dc.titleEvolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0129.pdf
Size:
1.02 MB
Format:
Adobe Portable Document Format
Download

Collections

Intelligent Decision Support and Big Data for Logistics and Supply Chain Management