{"id":1633,"date":"2020-11-29T21:42:18","date_gmt":"2020-11-29T20:42:18","guid":{"rendered":"https:\/\/tectron-worbis.de.46-4-53-222.vorschau.marketing-thom.de\/?page_id=1633"},"modified":"2023-03-20T10:58:38","modified_gmt":"2023-03-20T09:58:38","slug":"wissenschaftliches-engagement","status":"publish","type":"page","link":"https:\/\/www.tectron-worbis.de\/en\/unternehmen\/wissenschaftliches-engagement\/","title":{"rendered":"scientific commitment"},"content":{"rendered":"
SENECA<\/b><\/p>\n
Together with representatives of the Otto von Guericke University of Magdeburg (Chair of Logistic Systems, Artificial Intelligence Lab) and Thorsis Technologies GmbH, we are working on the development of a\u00a0\u201e<\/em><\/strong>self-learning decision support system for real-time order sequence and machine allocation planning\"<\/em><\/strong>. Essentially, the aim is to explore how machine learning (ML) methods can be applied to solve complex order sequencing and machine allocation problems under real-time conditions.conditions.<\/p>\n Increasing customer demands for flexibility and short notice with high on-time delivery rates are offset by higher production costs. Artificial intelligence methods can help generate real-time solutions that take both sides into account.<\/p>\n This project is supported by the Federal Ministry of Education and Research.<\/p>\n