{"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":"
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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

Further information can also be found at\u00a0http:\/\/www.seneca.ovgu.de\/<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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SENECA Gemeinsam mit Vertretern der Otto von Guericke Universit\u00e4t Magdeburg (Lehrstuhl f\u00fcr Logistische Systeme, Artificial Intelligence Lab) sowie der Firma Thorsis Technologies GmbH arbeiten wir an der Entwicklung eines\u00a0\u201eSelbstlernenden Entscheidungsunterstz\u00fctzungssystem f\u00fcr die echtzeitf\u00e4hige Auftragsreihenfolge- und Maschinenbelegungsplanung\u201c. Grunds\u00e4tzlich soll erforscht werden, wie Methoden des maschinellen Lernens (ML) angewendet werden k\u00f6nnen, um komplexe Auftragsreihenfolge- und Maschinenbelegungsprobleme unter […]<\/p>","protected":false},"author":1,"featured_media":0,"parent":388,"menu_order":10,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1633","page","type-page","status-publish","hentry"],"yoast_head":"\nwissenschaftliches Engagement – TECTRON WORBIS<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.tectron-worbis.de\/en\/unternehmen\/wissenschaftliches-engagement\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"wissenschaftliches Engagement – TECTRON WORBIS\" \/>\n<meta property=\"og:description\" content=\"SENECA Gemeinsam mit Vertretern der Otto von Guericke Universit\u00e4t Magdeburg (Lehrstuhl f\u00fcr Logistische Systeme, Artificial Intelligence Lab) sowie der Firma Thorsis Technologies GmbH arbeiten wir an der Entwicklung eines\u00a0\u201eSelbstlernenden Entscheidungsunterstz\u00fctzungssystem f\u00fcr die echtzeitf\u00e4hige Auftragsreihenfolge- und Maschinenbelegungsplanung\u201c. 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