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Dieter Weninger

Dr. Dieter Weninger, Akad. Rat

Department of Mathematics
Chair of Applied Mathematics (Mixed-Integer Linear and Nonlinear Optimization)

Room: Room 03.386
Cauerstraße 11
91058 Erlangen

Projects

  • Process optimization for hospital logistics

    (Third Party Funds Single)

  • 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)

    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.

  • 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

    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.

Publications

  • , , , , , , , , , , , , , , , , , , , , , :
    The SCIP Optimization Suite 5.0
    ()
    (Techreport)
  • , , , , , , , , , , , , , , , , , , , , :
    The SCIP Optimization Suite 4.0
    ()
    (Techreport)

Teaching

Übung (UE)

Vorlesung (VORL)

Hauptseminar (HS)