Logistics and Production

Logistics and Production

Personal planning, freight transport, network extensions, or charging of automatic teller machines are all state problems in logistics. Although the problems sound very different, they all have one thing in common. They are solvable by models and methods from discrete optimization. Why use 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 thought to be out of reach so far. The following projects document some of our success stories.

Ongoing Projects

Project “AUFSTIEG – Absicherung gegen Unsicherheiten für UAV-Flotten”

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 UAV service providers have an interest in optimizing UAV operations. This includes assigning UAVs to specific missions and routing them. Assignments and UAV routing are affected by uncertain data, such as wind or 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 account for uncertainties 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 cannot be solved 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 considerable energy, time, and replanning activities.

Consortium: TU Munich, FAU

Coordinator: TU Munich

Funding: BMWK

People involved: Florian Rösel, Frauke Liers

Contact: For further details on this project, please contact Florian Rösel (florian.roesel@fau.de).

Quality control by robust optimization within CRC 1411

more information

Finished Projects

HOTRUN – Holistic optimization of trajectories and runway scheduling

Project Information

Supported by

This project is part of the German BMWi research program „Fünftes ziviles Luftfahrtforschungsprogramm, 3. Aufruf“ (BMWi). This program sponsors the development of technologies to address various problems in the commercial aviation industry.

Description

The subproject „Entwicklung mathematischer Optimierungsmethoden für robustes Runway Scheduling“ (RobRun), executed by our group, aims to generate optimal schedules using discrete optimization methods while respecting aircraft trajectories. Furthermore, uncertainties will be considered by using techniques of robust optimization. This enables the user to compute trajectories and schedules that, for example, hedge against disruptions (within a predefined range) or recover as efficiently as possible after a disruption occurs.
Thus, the overall goal of this project is to combine trajectory and runway schedule computation, including resilience to uncertainties, to obtain stable flight routes and landings, respectively. take off, times. From a theoretical point of view, the relatively young field of robust optimization offers ample room for the development of new methods and the planned integration of optimal control (for trajectory planning) and combinatorial optimization problems (scheduling), which have hardly been investigated and hold great potential.

People involved

  • Benno Hoch
  • Frauke Liers

Contact

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 resources and operations in aviation

Project Information

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.

Supported by

This project is part of the German BMWi research program„Fünftes ziviles Luftfahrtforschungsprogramm, 3. Aufruf“ . This program sponsors the development of technologies to address various problems in the commercial aviation industry.

Description

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 aircraft, also under uncertain conditions and under disruptions. Therefore, it is necessary to find an effective algorithm that is structured so that the outcome is a practicable flight plan – even in the event 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 for conflicts among partial solutions, to find holistic solutions, and to realize benefits by leveraging dependencies.

People involved

  • Lukas Glomb
  • Frauke Liers
  • Florian Rösel

Contact

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.

Description

The main goal of HealthFaCT is to develop an innovative, software-aided system for optimization and decision-making to improve three essential pillars of medical care: pharmacies, emergency physicians, and ambulance scheduling. The main focus of this project lies on rural environments. Mathematical methodologies can make an important contribution by providing data-driven facts rather than political argumentation.
HealthFaCT is executed by

Furthermore, Apothekerkammer Nordrhein, Kreisverwaltung Mainz-Bingen, Stadtverwaltung Kaiserslautern, Gesundheitsamt der StädteRegion Aachen, Informatikgesellschaft für Software-Entwicklung mbH and Gesundheitsregion plus Erlangen-Höchstadt & Erlangen also take 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 the possibilities for expanding airport capacities are limited, one has to enhance the utilization of existing capacities in Air Traffic Management (ATM) to meet the ongoing growth in 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 for dealing with these changes is to regularly recompute or update 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 to changes in the data, leading to better resource utilization.

Participants: Andreas Heidt, Manu Kapolke, Frauke Liers