Dieter Weninger

Dr. Dieter Weninger, Akad. Rat

Department of Data Science (DDS)
Chair of Analytics & Mixed-Integer Optimization

Room: Raum 03.386
Cauerstraße 11
91058 Erlangen

• Phone number: +49 9131 85-67188
• Fax number: +49 9131 85-67162
• Email: dieter.weninger@math.uni-erlangen.de

Projects

• Process optimization for hospital logistics

  (Third Party Funds Single)

  Term: since 01-01-2020
  Funding source: Industrie
  URL: https://en.www.math.fau.de/edom/projects-edom/logistics-and-production/process-optimization-for-hospital-logistics/
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• Decomposition methods for mixed-integer optimal control (A05) (2018 - 2022)

  (Third Party Funds Group – Sub project)

  Overall project: TRR 154: Mathematische Modellierung, Simulation und Optimierung am Beispiel von Gasnetzwerken
  Term: 01-07-2018 - 30-06-2022
  Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)
  Abstract

  The focus lies on the development of mathematical decomposition methods for mixed-integer nonlinear optimal control problems on networks. On the top level (master) mixed-integer linear problems are in place, whereas in the sub-problem only continuous variables are considered. The exchange between the levels is performed not only via cutting planes, but also via the modelling of disjunctions to deal with non-convex optimal control problems as well. The overall emphasis is the mathematical analysis of structured mixed nonlinear optimization problems based on hierarchical models.

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• Development of new Linear and Integer Programming Techniques to solve Supply Chain Management Problems

  (Third Party Funds Single)

  Term: since 01-03-2010
  Funding source: Industrie
  Abstract

  Supply Chain Management (SCM) deals with the combination of procurement, production, storage, transport and delivery of commodities. Problems of this kind occur in all kinds of industry branches. Since the integrated planning of these processes contains a high potential for optimization it is of great importance for the companies’ efficiency.

  The method of choice to find optimal solutions in SCM is linear and integer programming. Nevertheless, there are big challenges to overcome – concerning both hardware and algorithms – due to very detailed and therefore large models. Additionally there may occur numerical difficulties that standard techniques cannot deal with.

  As a consequence, the problem’s mathematical formulation has to be done carefully and new methods need to be implemented to improve the performance of MIP algorithms.

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Publications

Teaching