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.
| Author: | Gerald GamrathORCiD, Ambros GleixnerORCiD, Thorsten KochORCiD, Matthias MiltenbergerORCiD, 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 |
