Junior Professorship for

"Artificial Intelligence (AI) -

Application in Production and Logistics"

AI Engineering (AiEng) encompasses the systematic design, development, integration and operation of solutions based on artificial intelligence (AI) using engineering methods as a model. At the same time, AiEng builds a bridge between basic research on AI methods and the engineering sciences and makes the use of AI systematically accessible and available there. The project focuses on the nationwide development of an "AI Engineering" Bachelor's degree program, which combines the training of AI methods, models and technologies with those of engineering sciences. AiEng is to be designed as a cooperative study program between Otto von Guericke University (OVGU) Magdeburg and the four universities in Saxony-Anhalt: Anhalt University of Applied Sciences, Harz University of Applied Sciences, Magdeburg-Stendal University of Applied Sciences and Merseburg University of Applied Sciences. The interdisciplinary degree program will enable students to develop AI systems and services in the industrial environment and beyond and to provide holistic support for the associated engineering process - from problem analysis to commissioning and maintenance / servicing. The AiEng curriculum provides comprehensive AI training, supplemented by basic engineering training and in-depth training in a selected application domain. In order to achieve a symbiosis of AI and engineering education, a new action-oriented framework is developed and taught, which describes the complete engineering process of AI solutions and methodically supports all phases. AIEng is characterized by a cross-module interlocking of teaching and learning content within a semester as well as by a cross-faculty and cross-university tandem teaching concept and pursues a student-centered didactic concept, which is supported by many practice-oriented (team) projects and a wide range of Open Educational Resources (OERs) with an (e)-tutor program.

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The research project SENECA pursues the development of a self-learning decision support system for real-time order sequencing and machine scheduling. The research question is how machine learning (ML) methods must be applied in order to calculate admissible solutions with sufficient quality for order sequencing and machine allocation problems in real time. Various ML methods are to be investigated with regard to their applicability for order sequencing and machine allocation planning. Due to the highly dynamic nature of modern production systems and the resulting planning uncertainty, it is expected that production sequence planning in particular will benefit from ML-based, real-time capable and adaptive decision support systems. ML algorithms are currently primarily used for regression and classification problems. Their direct use for calculating optimization problems has hardly been researched and industrial applications are not yet known. The technical objective of the project is to develop a software and hardware prototype that supports decision-makers in production planning and control. The technical challenges relate in particular to aspects of production and application-specific design. On the one hand, a high level of user-friendliness is important. This implies, among other things, that humans are always the final decision-making authority. The system should be able to continuously improve itself with human expertise. On the other hand, the assistance system must be designed in such a way that the real-time capability of the solution processes is fully utilized. Proposed order sequences and machine assignments must be able to be transferred from production planning to production control at short notice.

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ProdLog addresses the issue of a weak industrial sector in Kazakhstan, Kyrgyzstan and Russian Federation and focuses on enabling universities to gain and provide a profound and holistic knowledge on planning and operating sustainable production processes. For that purpose a bologna-based master curriculum with 18 modules in resource efficient production logistics will be developed and implemented in six universities of the partner countries. The academic staff will be trained with innovative teaching methods in the learning factory "Technology centre for production and logistics systems PULS" and  equipped with state of the art logistics laboratories. By means of that, the understanding of logistics shall be widened - away from transport logistics to a systemic and interdisciplinary approach of applicant-oriented education, challenges with economical, political and social problems of our society.

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Lastenräder sind eine nachhaltige Alternative für den Transport von Waren in Städten. Sie haben das Potenzial zur Substitution von 25% der heutigen innerstädtischen Lieferfahrten und können so zu CO₂-Einsparungen und einer höheren Lebensqualität in Städten beitragen. Das Einrichten von Innenstadtdepots für Lastenräder ermöglicht die Lagerung und den Umschlag von Waren für die anschließende Verteilung per Lastenrad in der Stadt. In dem interdisziplinären Projekt "Lastenraddepot" wird ein modellhafter Leitfaden zur Implementierung von Innenstadtdepots entwickelt. Der Fokus liegt sowohl auf logistischen Anforderungen, der Gewährleistung des Verkehrsflusses und einer hohen Akzeptanz durch Stakeholder. Es werden Aspekte wie Standortfragen, die Wirkung eines hohen Lastenradaufkommens im Verkehr, die Akzeptanz bei Anwohnenden und Verkehrsteilnehmenden sowie Nutzungspräferenzen von Lastenradfahrenden untersucht.
Der Lehrstuhl Logistische Systeme bildet gemeinsam mit der Abteilung Umweltpsychologie am Institut für Psychologie ein interdisziplinäres Team. Während auf logistischer Seite Verkehrsräume modelliert und simuliert werden, sind im Bereich der psychologischen Akzeptanzforschung eine qualitative Befragung von Sachverständigen (z.B. aus Lieferbranche, Planung, kommunalen Verwaltungen) und eine quantitative Befragung einer für Städte repräsentativen Stichprobe geplant.
Das Vorhaben zielt im Sinne des Nationalen Radverkehrsplans 2020 auf eine Verbesserung der Verkehrsqualität, eine Sicherung nachhaltiger Mobilität, eine breite Anwendbarkeit der Ergebnisse und die Generierung neuer Erkenntnisse. Es wird durch das Bundesministerium für Verkehr und digitale Infrastruktur (BMVI) aus Mitteln zur Umsetzung des Nationalen Radverkehrsplans 2020 gefördert.
Dem Projekt steht ein Projektbeirat zur Seite. Dieser besteht aus den folgenden Mitgliedern:
- Cargobike.jetzt
- Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
- DPD Deutschland GmbH
- PedalPower Schönstedt&Busack GbR
- United Parcel Service (UPS)
- Stadt Köln
- Zentrum für angewandte Psychologie, Umwelt- und Sozialforschung (ZEUS GmbH).

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