The Project
Development of a self-learning decision support system for real-time job sequence and machine allocation planning
The research project SENECA aims at the development of a self-learning decision support system for real-time capable job sequence and machine allocation planning. The central research question is how machine learning (ML) methods have to be applied to compute in real-time acceptable solutions with sufficient quality for job sequence and machine allocation problems.
In the SENECA research project, various ML methods are to be examined with regard to their applicability for job sequence and machine allocation planning. Due to the high dynamics of modern production systems and the resulting planning uncertainty, it is expected that production process planning in particular will benefit from real-time capable and adaptive decision support systems.
The research findings are to be implemented as a prototype in an assistance system of the same name, which is to be evaluated on the basis of the production planning of the project partner Tectron GmbH. The illustration outlines the structure of SENECA:
It is a project within the framework of the BMBF guideline for the funding of projects on the subject of "Anwendung von Methoden der Künstlichen Intelligenz in der Praxis".
