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

  • The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems. The complexity of industrial-scale supply chain optimization, however, often poses limits to the application of general mixed-integer programming solvers. In this paper we describe algorithmic innovations that help to ensure that MIP solver performance matches the complexity of the large supply chain problems and tight time limits encountered in practice. Our computational evaluation is based on a diverse set, modeling real-world scenarios supplied by our industry partner SAP.

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Metadaten
Author:Gerald Gamrath, Ambros Gleixner, Thorsten Koch, Matthias Miltenberger, Dimitri Kniasew, Dominik Schlögel, Alexander Martin, Dieter Weninger
Document Type:Article
Parent Title (English):Journal of Computational Mathematics
Volume:37
First Page:866
Last Page:888
Year of first publication:2019
Preprint:urn:nbn:de:0297-zib-61107
DOI:https://doi.org/10.4208/jcm.1905-m2019-0055