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

Third party funded individual grant


Project Details

Project leader:
Prof. Dr. Alexander Martin

Project members:
Dr. Dieter Weninger
Katrin Halbig

Contributing FAU Organisations:
Economics - Discrete Optimization - Mathematics (EDOM)

Funding source: Industrie
Start date: 01/03/2010


Research Fields

Mixed Integer Programming
Economics - Discrete Optimization - Mathematics (EDOM) Professur für Angewandte Mathematik (Ganzzahlige und robuste Optimierung)


Abstract (technical / expert description):

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.


External Partners

Konrad-Zuse-Zentrum für Informationstechnik / Zuse Institute Berlin (ZIB)


Publications

Gamrath, G., Gleixner, A., Koch, T., Miltenberger, M., Kniasew, D., Schlögel, D.,... Weninger, D. (2019). Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming. Journal of Computational Mathematics.
Chen, W., Gemander, P., Gleixner, A., Gottwald, L., Martin, A., & Weninger, D. (2019). Two-row and two-column mixed-integer presolve using hash-based pairing methods.
Gamrath, G., Koch, T., Martin, A., Miltenberger, M., & Weninger, D. (2015). Progress in presolving for mixed integer programming. Mathematical Programming Computation, 7(4), 367-398. https://dx.doi.org/10.1007/s12532-015-0083-5

Last updated on 2019-30-08 at 10:26