Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming

Journal article


Publication Details

Author(s): Gamrath G, Gleixner A, Koch T, Miltenberger M, Kniasew D, Schlögel D, Martin A, Weninger D
Journal: Journal of Computational Mathematics
Publishing place: Takustr. 7, 14195 Berlin
Publication year: 2019
ISSN: 0254-9409
Language: English


Abstract

SAP's decision support systems for optimized supply network planning rely on mixed-integer programming as the core engine to compute optimal or near-optimal solutions. The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of a robust and future-proof decision support system for a large and diverse customer base. In this paper we describe our coordinated efforts to ensure that the performance of the underlying solution algorithms matches the complexity of the large supply chain problems and tight time limits encountered in practice.


FAU Authors / FAU Editors

Martin, Alexander Prof. Dr.
Economics - Discrete Optimization - Mathematics (EDOM)
Weninger, Dieter Dr.
Economics - Discrete Optimization - Mathematics (EDOM)


External institutions with authors

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


How to cite

APA:
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.

MLA:
Gamrath, Gerald, et al. "Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming." Journal of Computational Mathematics (2019).

BibTeX: 

Last updated on 2019-14-08 at 16:38