Logistics and Production
Logistics and Production
Personal planning, freight transport, network extentions or charging of automatic teller machines, all state problems in logistics. Although the problems sound very different, all of them have one thing in common. They are solvable by models and methods from discrete optimization. Why using discrete optimization methods? Because they provide provably good solutions, because they can approximate the potential of further improvements, and because this way new strategies can be developed producing solutions that were tought to be out of reach so far. The following projects document some of our success stories.
Practical applications of “electric vertical take-off and landing unmanned aerial vehicles” (eVTOL UAV, aka. “drones”) abound. They promise enormous efficiency gains in, e.g., measurement, observation and maintenance processes. We expect that in the near future the fleets of UAV service providers will grow considerably. We further expect that the UAV service providers have an interest in optimizing the operations of UAVs. This includes the assignment of UAVs to specific missions, as well as the routing of the UAVs. Assignment, as well as routing of UAVs, are affected by uncertain data, such as wind or uncertain mission duration. Existing methods assign UAVs to missions first and optimize their routing afterwards. Many existing methods ignore the uncertainties which typically leads to suboptimal – or even infeasible – results when uncertainties manifest themselves unfavorably. We expect considerable efficiency gains by optimizing mission assignment and routing simultaneously while developing algorithms that take the uncertainties into account and hedge against them. Hence, we develop (distributionally) robust optimization algorithms for simultaneous mission assignment and UAV routing in Project AUFSTIEG. Due to their size and complexity, the resulting optimization problems are not solvable by standard methods. Hence, we develop tailored decomposition methods with quality guarantees to tackle the problem. Approaches are validated and calibrated with realistic data provided by our associate partners. The goal is that the developed methods will save considerably in energy, time and replanning activities.
Consortium: TU Munich, FAU
Coordinator: TU Munich
- Florian Rösel
- Frauke Liers
For further details on this project, please contact Florian Rösel (firstname.lastname@example.org).
Quality control by robust optimization within CRC 1411
HOTRUN – Holistic optimization of trajectories and runway scheduling
- Contracting Entity: Federal Ministry of Economic Affairs and Energy (BMWi)
- Project Duration: 36 months, September 2018 – August 2021
- HOTRUN is executed by our group and the Institute of Flight Systeme dynamics at the TUM in Munich (Chair: Prof. Dr. -Ing Florian Holzapfel )
This project is part of the German BMWi research program „Fünftes ziviles Luftfahrtforschungsprogramm, 3. Aufruf“ (BMWi). This program sponsors the development of technologies, which can be applied to solve various problems of the commercial aviation industry.
The subproject „Entwicklung mathematischer Optimierungsmethoden für robustes Runway Scheduling“ (RobRun) executed by our group, aims at generating optimal schedules using discrete optimization methods and respecting aircraft trajectories. Furthermore, uncertainties will be considered by using techniques of robust optimization. This enables the user to compute trajectories and schedules which, for example, hedge against disruptions (in a predefined range), or are able to recover as efficient as possible after a disruption occurred.
Thus, the overall goal of this project is to combine trajectory and runway schedule computation, including resilience against uncertainties, in order to obtain stable flight routes and landing, resp. take off, times. From a theoretical point of view, the relatively young field of robust opimization offers a lot of space for the development of new methods and the planned integrated solution of optimal control (for trajectory planning) and combinatorial optimization problems (scheduling) has hardly been investigated and bears great potential.
- Benno Hoch
- Frauke Liers
For further details on this project, please contact Benno Hoch (benno.hoch[at]fau.de).
If you are interested in information about the BMWi’s aviation research program, click here.
OPs-TIMAL – Optimized processes for trajectory, maintenance and management of ressources and operations in aviation
Contracting Entity: Federal Ministry of Economic Affairs and Energy (Opens external link in new windowBMWi)
Project Duration: 42 months, January 2018 – June 2021
OPs-TIMAL is executed by four universities and research institutes and nine industrial partners.
This project is part of the German BMWi research program „Fünftes ziviles Luftfahrtforschungsprogramm, 3. Aufruf“ . This program sponsors the development of technologies, which can be applied to solve various problems of the commercial aviation industry.
Within the joint research project OPs-TIMAL the FAU has two main contributions. The first one is to provide robust solutions for fleeting and routing aircrafts, also under uncertain conditions and under disruptions. Therefore it is necessary to find an effective algorithm, which should be structured in a way, that the outcome is a practicable flight plan – even in case of major disruptions. The second main part executed by the FAU is to supervise the combination of the partial solutions obtained by the project partners in a holistic optimization framework. Goals are to detect and compensate the conflicts between the partial solutions, to find holistic solutions and to realize benefits by taking advantage of dependencies.
- Lukas Glomb
- Frauke Liers
- Florian Rösel
For further details on this project, please contact Florian Rösel (florian.roesel[at]fau.de) or Lukas Glomb (lukas.glomb[at]fau.de).
If you are interested in information about the BMWi’s aviation research program, visit.
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 Network Design
In this project, we address a robust network design problem where the traffic demands change over time. For k different times of the day, we are given for each node the single-commodity flow it wants to send or to receive. The task is to determine the minimum-cost edge capacities such that the flow can be routed integrally through the net at all times.
Participants: Frauke Liers
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