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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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

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Problem definition

Local public transport in rural areas is often poorly developed. Mobility stations can make public transport more attractive by providing a flexible and easily accessible transfer point between demand-oriented modules such as bikes, cars, buses and trains. However, the planning of these modules and the provision of mobility information for users has so far been individualized. Digitally available information on the stations (e.g. number of free car and bicycle parking spaces) is hardly available and cannot be retrieved in a standardized way. This delays the switch to environmentally friendly modes of transport on the supply and data side.

Project objective
A modular concept is being developed for a smart mobility station in the district of Mansfeld-Südharz. This is to consist of a total of three core components.
Smart mobility station - these will be equipped with an infotainment system that can be used to obtain tourist or timetable-related information.
Modular mobility station - the decisive advantage of the system for municipalities is that the modules are standardized and can be exchanged if necessary.
Open source planning tool - the digital data and the modular structure are to be made available in this tool.
A prototype is to be built and tested at a site in the Mansfeld-Südharz district.

Implementation

In the project, regional partners in the application region work together with supra-regional partners on an interdisciplinary basis. The first step is to define the requirements and interfaces for a smart mobility station in order to achieve the objectives. Subsequently, the partners will work in teams to develop the smart components, the modular station and the planning tool for it in parallel. In the final phase of the project, the prototype will be implemented in a field test with the construction of a functional model and the validation of the smart components and data exchange.
This text was translated with DeepL

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