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

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...

Full description

Saved in:
Bibliographic Details
Published in:Procedia manufacturing 2017, Vol.9, p.25-32
Main Authors: Tisch, M., Laudemann, H., Kreß, A., Metternich, J.
Format: Article
Language:English
Subjects:
competency development
learning factory
technical system
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2351-9789
2351-9789
DOI:10.1016/j.promfg.2017.04.017