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.

Ongoing 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, which can be applied to solve various problems of the commercial aviation industry.

Description

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.

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 ressources 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, which can be applied to solve various problems of 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 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.

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 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.

Partners

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 take also part in HealthFaCT. The HealthFaCT team also cooperates with IBOSS.

 

Poster

Process optimization for hospital logistics

Project Information

  • Contracting Entity: OrgaCard Siemantel & Alt GmbH
  • Minimal Project Duration: 24 months, January 2020 – December 2021

Description

This project aims at developing solution methods for problems arising in the logistics management of the public health care sector. More specifically, transport orders in hospitals or other medical facilities should be allocated to employees in order to generate a plan of transport, that also incorporates the routes and execution times of all orders. In this plan, the routing of the transport orders shall be computed considering the infectiousness of the patients, the properties of the means of transportation and, among other requirements, the location of the responsible employee. Moreover the scheduled plan shall minimize order delays, transport distances and the burden of executing employees or other configurable criteria specified by the customers of OrgaCard.
Thus, the overall goal of this project is to organize transport logistics of medical facilities in an integrated mathematical framework in real time, using techniques of machine learning and combinatorial respectively discrete optimization.

News

 

The DMEA Young Talent Award is presented annually to the best Bachelor and Master theses in the fields of medical informatics, e-health, health IT, health management, health economics and healthcare management. In 2020, Alexander Müller’s master’s thesis “Ein Spaltenerzeugungsalgorithmus zur Optimierung von Transporten im Krankenhaus“ took first place in the master’s thesis category.

Link: https://www.dmea.de/Presse/News-Blog/Artikel/DMEA-Nachwuchspreis2020DieDiesjaehrigenGewinnerinnenUndGewinner.html

Alexander Müller, as last years winner of the DMEA Young Talent Award,
was interviewed by the organisers of the DMEA.

The interview can be read here:
https://www.dmea.de/Presse/News-Blog/Artikel/DMEA-NachwuchspreistraegerImPortraet.html

People involved

Contact

Link:  https://www.orgacard.de/software-loesungen/logistik/logistik-management/

Finished Projects

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