Jun.-Prof. Dr.-Ing. Sebastian Lang

Junior Professorship

Junior Professorship for »Artificial Intelligence (AI) - Application in Production and Logistics«
G10-255
Project overview

Current projects
AI Engineering - An interdisciplinary, project-oriented bachelor's degree program with a focus on artificial intelligence and engineering.
Duration: 01.12.2021 bis 30.11.2025

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.

View project in the research portal

Completed projects
24401_ SENECA - Development of a self-learning decision support system for real-time order sequencing and machine scheduling
Duration: 01.04.2020 bis 31.03.2022

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.

View project in the research portal

Development of a Bologna-based Master Curriculum in Resource Efficient Production Logistics (ProdLog)
Duration: 01.12.2017 bis 15.10.2021

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.

View project in the research portal

Cargo bike depot - "Citizen- and traffic-friendly implementation of inner-city depots for cargo bikes"
Duration: 01.09.2017 bis 30.11.2019

Cargo bikes are a sustainable alternative for transporting goods in cities. They have the potential to replace 25% of today's inner-city delivery trips and can thus contribute to CO2 savings and a higher quality of life in cities. Setting up inner-city depots for cargo bikes enables the storage and handling of goods for subsequent distribution by cargo bike in the city. In the interdisciplinary project "Cargo bike depot", a model guideline for the implementation of inner-city depots is being developed. The focus is on logistical requirements, ensuring the flow of traffic and a high level of acceptance by stakeholders. Aspects such as location issues, the effect of a high volume of cargo bikes in traffic, acceptance by residents and road users as well as usage preferences of cargo bike riders are being investigated.
The Chair of Logistical Systems forms an interdisciplinary team together with the Department of Environmental Psychology at the Institute of Psychology. While traffic areas are modeled and simulated on the logistics side, a qualitative survey of experts (e.g. from the delivery industry, planning, municipal administrations) and a quantitative survey of a sample representative of cities are planned in the area of psychological acceptance research.
In line with the National Cycling Plan 2020, the project aims to improve traffic quality, ensure sustainable mobility, ensure broad applicability of the results and generate new findings. It is funded by the Federal Ministry of Transport and Digital Infrastructure (BMVI) from funds for the implementation of the National Cycling Plan 2020.
The project is supported by a project advisory board. This consists of the following members:
- Cargobike.now
- German Aerospace Center (DLR)
- DPD Germany GmbH
- PedalPower Schönstedt&Busack GbR
- United Parcel Service (UPS)
- City of Cologne
- Center for Applied Psychology, Environmental and Social Research (ZEUS GmbH).
This text was translated with DeepL

View project in the research portal

Publications
Courses

Last Modification: 11.03.2025 - Contact Person: Webmaster