Self-learning decision support system for real-time job sequence and machine allocation planning

SENECA

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.

 

Weekly Jour Fixe

28.01.2021 -

Despite strict Covid19 requirements, the project team is constantly working on the SENECA project. Attached is a screenshot from the weekly Jour Fixe.

 

 Jour Fixe

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Publication submitted

13.01.2021 -

A Paper for the INCOM 2021 conference of the International Federation of Automatic Control (IFAC) has been submitted.

https://incom2021.org/

Project progress

09.09.2020 -

The project team is currently working on sub-project 2 "Conception and software implementation".

Further information to Seneca

Our project team from OvGU

    The partners in the project

    Last Modification: 03.12.2020 - Contact Person: Paul Reichardt