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Utility-based Configuration of Learning Factories Using a Multidimensional, Multiple-choice Knapsack Problem

Tisch, Michael ; Laudemann, Heiko ; Kreß, Antonio ; Metternich, Joachim :
Utility-based Configuration of Learning Factories Using a Multidimensional, Multiple-choice Knapsack Problem.
[Online-Edition: https://doi.org/10.1016/j.promfg.2017.04.017]
In: Procedia Manufacturing, 7th Conference on Learning Factories, Darmstadt, Elsevier B.V., 9 S. 25-32. ISSN 2351-9789
[Artikel] , (2017)

Offizielle URL: https://doi.org/10.1016/j.promfg.2017.04.017

Kurzbeschreibung (Abstract)

The paper presents a structural approach to configure the technical system of a learning factory by considering learning targets and maximizing the utility. Local scope conditions and intended competencies are used to operationalize requirements. The composition of the module-based technical system can be optimized by maximizing its overall utility. Therefore, an exact and efficient optimization algorithm is developed solving a multidimensional multiple-choice knapsack problem combined with a two-dimensional bin packing problem. Restrictions are the available budget and the useable area of the learning factory. As a result, the configured technical system enables optimal target orientation of the learning factory. This procedure is finally applied on the Process Learning Factory CiP.

Typ des Eintrags: Artikel
Erschienen: 2017
Autor(en): Tisch, Michael ; Laudemann, Heiko ; Kreß, Antonio ; Metternich, Joachim
Titel: Utility-based Configuration of Learning Factories Using a Multidimensional, Multiple-choice Knapsack Problem
Sprache: Englisch
Kurzbeschreibung (Abstract):

The paper presents a structural approach to configure the technical system of a learning factory by considering learning targets and maximizing the utility. Local scope conditions and intended competencies are used to operationalize requirements. The composition of the module-based technical system can be optimized by maximizing its overall utility. Therefore, an exact and efficient optimization algorithm is developed solving a multidimensional multiple-choice knapsack problem combined with a two-dimensional bin packing problem. Restrictions are the available budget and the useable area of the learning factory. As a result, the configured technical system enables optimal target orientation of the learning factory. This procedure is finally applied on the Process Learning Factory CiP.

Titel der Zeitschrift, Zeitung oder Schriftenreihe: Procedia Manufacturing, 7th Conference on Learning Factories, Darmstadt, Elsevier B.V.
Band: 9
Freie Schlagworte: learning factory, technical system, competency development
Fachbereich(e)/-gebiet(e): 16 Fachbereich Maschinenbau
16 Fachbereich Maschinenbau > Institut für Produktionsmanagement, Technologie und Werkzeugmaschinen (PTW)
16 Fachbereich Maschinenbau > Institut für Produktionsmanagement, Technologie und Werkzeugmaschinen (PTW) > CiP Center für industrielle Produktivität
Hinterlegungsdatum: 01 Sep 2017 11:28
DOI: https://doi.org/10.1016/j.promfg.2017.04.017
Offizielle URL: https://doi.org/10.1016/j.promfg.2017.04.017
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