Data is often thought of as the resource of the future. The enormous amount of data that is currently available leads to new scientific and economic questions, such as: “How can companies extract useful information from the available data to support their business processes,“ or “How can analysis of the available data improve medical diagnostics?“ The field of research that concerns itself with such questions is called ‘”Analytics” and can be divided into the following areas:
- Descriptive Analytics (“What is the current state?”)
- Predictive Analytics (“What developments are most likely?”)
- Prescriptive Analytics (“What should we do?”)
At present “Analytics” combined with buzzwords like “Big Data”, “Internet of Things” or “Smart Data” is a widely discussed topic in the scientific world as well as in business. However, most of the discussions focus just on the first two of the above mentioned subfields: How can we gather this data? How can we recognize its essential characteristics? How will it evolve in the future? Yet the underrepresented third subject is the one with the biggest potential: How can we use this data to assist us in decision making and how can we choose among different alternatives in order to optimize processes or even identify completely new business segments or develop new business models. In other words, what better ways are there to make use of data in mathematical optimization? In our working group we address exactly this question. The following projects provide further insight into our work on this topic.
ADA Lovelace Center for Analytics, Data and Applications
Artificial Intelligence (AI) has long left behind its character as a solely theoretic discipline and permeates more and more our daily life. It enables digital assistants, cooperating robots as well as vastly autonomous vehicles and production facilities. The ADA Lovelace Center for Analytics, Data and Applications has been founded by the Fraunhofer Institute for Integrated Circuits (IIS), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Ludwig-Maximilians-Universität München (LMU). It is a partner for the national and international industry whose aim it is to help them benefit from these developments and to make them economically usable.
Optimal Control of Electrical Distribution Networks with Uncertain Solar Feed-In
“Areic modelling, simulation and optimization of solar feed-in, power flow and control of electrical distribution networks with uncertain feed” is a collaborative project of the BMBF-funding measure “Mathematics for Innovations”. The project will be funded from January 01, 2018 to Dezember 31, 2020.
- Kevin-Martin Aigner, Frauke Liers, Alexander Martin
For further details about this project please contact Kevin-Martin Aigner (firstname.lastname@example.org).
- Universität Ulm, Institut für Stochastik (Prof. Dr. Volker Schmidt, project coordinator)
- Universität Duisburg-Essen, Lehrstuhl für Energiewissenschaft (Prof. Dr. Christoph Weber)
Optimization of medical care in rural environments
HealthFaCT – Health: Facility Location, Covering, and Transport is a collaborative project of the BMBF-funding measure “Mathematics for Innovations in Industry and Services”.
HealthFaCT will be funded from December 01, 2016 to November 30, 2019.
The main goal of HealthFaCT is the development of an innovative and software-aided system for optimization and decision making to improve three essential pillars of medical care: pharmacies, emergency physicians as well as scheduling of ambulances. The main focus of this project lies on rural environments. Mathematical methodologies can make an important contribution in terms of data-driven facts rather than political argumentation. Detailed information about the contents of this project can be found here.
HealthFaCT is executed by
- University of Erlangen-Nuremberg (Dennis Adelhütte, Prof. Dr. Frauke Liers (project coordinator), Sebastian Tschuppik),
- RWTH Aachen University (Prof. Dr. Christina Büsing, Timo Gersing),
- TU Kaiserslautern (Prof. Dr. Sven O. Krumke, Eva Schmidt, Manuel Streicher)
- Fraunhofer Institute for Industrial Mathematics ITWM (Melanie Heidgen, Dr. Neele Leithäuser, Johanna Schneider).
Furthermore, Apothekerkammer Nordrhein, Kreisverwaltung Mainz-Bingen, Stadtverwaltung Kaiserslautern, Gesundheitsamt der StädteRegion Aachen, Informatikgesellschaft für Software-Entwicklung mbH and Gesundheitsregion plus Erlangen-Höchstadt & Erlangen take also part in HealthFaCT. The HealthFaCT team also cooperates with IBOSS.
Robust Schedules for Air Traffic Management
Increasing air traffic and new procedures in air traffic management require a very efficient use of limited ATM resources. It is impossible to create schedules for future use which never need to be adapted. Reasons are e.g., unexpected weather conditions, late passengers, and intended and unintended deviations from schedules. We tackle scheduling problems in ATM, like the planning of airplanes on runways. Therefore, the focus of the assigned task lies on modeling, understanding and controlling uncertainty in ATM problems. So it is important to concern with Resilience and Adaptation to continue having air transport and to be competitive to alternative transportation. Thus we have to accept these phenomena and have to incorporate uncertainty into the model.
Participants: Andreas Heidt
RobustATM: Robust Optimization of ATM Planning Processes by Modelling of Uncertainty Impact
As possibilities of enlarging airport capacities are limited, one has to enhance the utilization of existing capacities in Air Traffic Management (ATM) to meet the continuous growth of traffic demand. Therefore, it is crucial for the performance of the whole ATM System that the traffic on a runway is planned efficiently. However, uncertainty, inaccuracy and non-determinism almost always lead to deviations from the actual plan or schedule. A typical strategy to deal with these changes is a regular re-computation or update of the schedule. These adjustments are performed in hindsight, i.e. after the actual change in the data occurred. The challenge is to incorporate uncertainty into the initial computation of the plans so that these plans are robust with respect to changes in the data, leading to a better utilization of resources.
Participants: Andreas Heidt, Manu Kapolke, Frauke Liers