Stammdaten

Titel: An Artificial Hormone-based Algorithm for Production Scheduling from the Bottom-up
Untertitel:
Kurzfassung:

This paper presents a model for supporting a production scheduling system with an artificial hormone algorithm. The system consists of lots that have to undergo a number of processing steps on different machines. The processing steps for a lot are formalized in a recipe assigned to the lot type. Since the steps in the recipe have to be processed in order, the given system allows choice only in the context of selecting a particular machine for the next step and in changing the processing order of waiting lots at a machine. Optimization of such a job-shop scheduling system is an NP-hard problem. In the approach proposed by this paper, artificial hormone systems are used to express the urgency of a lot and the need for new lots at a machine type, thus providing a system using local information for optimization. Results indicate that the artificial hormone system provides an improvement of around 5% over a First Come-First Serve approach.

Schlagworte: Production Scheduling, Job-shop Scheduling, Artificial Hormone Systems, Self-organizing Systems, SwarmSystems
Publikationstyp: Beitrag in Proceedings (Autorenschaft)
Erscheinungsdatum: 04.02.2021 (Online)
Erschienen in: Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021)
Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021)
zur Publikation
 ( SciTePress - Science and Technology Publications; A. Rocha, L. Steels, J. van den Herik )
Titel der Serie: -
Bandnummer: 1
Erstveröffentlichung: Ja
Version: -
Seite: S. 296 - 303

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Keine Version vorhanden
Erscheinungsdatum: 04.02.2021
ISBN (e-book): -
eISSN: -
DOI: http://dx.doi.org/10.5220/0010243902960303
Homepage: https://www.scitepress.org/Papers/2021/102439/102439.pdf
Open Access
  • Online verfügbar (Open Access)

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Vernetzte und Eingebettete Systeme
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
Österreich
  -993640
   kornelia.lienbacher@aau.at
https://nes.aau.at/
zur Organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 102009 - Computersimulation
  • 202022 - Informationstechnik
  • 202041 - Technische Informatik
Forschungscluster
  • Selbstorganisierende Systeme
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Smart Grids Group

Kooperationen

Organisation Adresse
Lakeside Labs GmbH
Lakeside Science & Technology Park
9020 Klagenfurt
Österreich - Kärnten
Lakeside Science & Technology Park
AT - 9020  Klagenfurt

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