Tisch, Michael ; Laudemann, Heiko ; Kreß, Antonio ; Metternich, Joachim (2017)
Utility-based Configuration of Learning Factories Using a Multidimensional, Multiple-choice Knapsack Problem.
In: Procedia Manufacturing, 7th Conference on Learning Factories, Darmstadt, Elsevier B.V., 9
doi: 10.1016/j.promfg.2017.04.017
Article
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.
| Item Type: | Article |
|---|---|
| Erschienen: | 2017 |
| Creators: | Tisch, Michael ; Laudemann, Heiko ; Kreß, Antonio ; Metternich, Joachim |
| Type of entry: | Bibliographie |
| Title: | Utility-based Configuration of Learning Factories Using a Multidimensional, Multiple-choice Knapsack Problem |
| Language: | English |
| Date: | 2017 |
| Journal or Publication Title: | Procedia Manufacturing, 7th Conference on Learning Factories, Darmstadt, Elsevier B.V. |
| Volume of the journal: | 9 |
| DOI: | 10.1016/j.promfg.2017.04.017 |
| URL / URN: | https://doi.org/10.1016/j.promfg.2017.04.017 |
| 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. |
| Uncontrolled Keywords: | learning factory, technical system, competency development |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > CiP Center for industrial Productivity |
| Date Deposited: | 01 Sep 2017 11:28 |
| Last Modified: | 24 Jan 2022 10:52 |
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