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Utility-based configuration of learning factories using a multidimensional, multiple-choice knapsack problem

Tisch, Michael and Laudemann, Heiko and Kreß, Antonio and Metternich, Joachim (2017):
Utility-based configuration of learning factories using a multidimensional, multiple-choice knapsack problem. (Publisher's Version)
In: Procedia Manufacturing, 9, pp. 25-32. ISSN 2351-9789,
DOI: 10.25534/tuprints-00014287,
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Item Type: Article
Origin: Secondary publication service
Status: Publisher's Version
Title: Utility-based configuration of learning factories using a multidimensional, multiple-choice knapsack problem
Language: English
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.

Journal or Publication Title: Procedia Manufacturing
Journal volume: 9
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > CiP Center for industrial Productivity
Date Deposited: 30 Nov 2020 14:45
Last Modified: 02 Dec 2020 07:06
DOI: 10.25534/tuprints-00014287
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-142879
Additional Information:

7th Conference on Learning Factories, CLF 2017

URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/14287
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