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

Gamrath G, Gleixner A, Koch T, Miltenberger M, Kniasew D, Schlögel D, Martin A, Weninger D (2019)

Publication Language: English

Publication Type: Journal article

Publication year: 2019


City/Town: Takustr. 7, 14195 Berlin

URI: https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/6110

DOI: 10.4208/jcm.1905-m2019-0055


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.

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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. https://dx.doi.org/10.4208/jcm.1905-m2019-0055


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

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