@Article{JCM-37-866,
author = {Gamrath , GeraldGleixner , AmbrosKoch , ThorstenMiltenberger , MatthiasKniasew , DimitriSchlögel , DominikMartin , Alexander and Weninger , Dieter},
title = {Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming},
journal = {Journal of Computational Mathematics},
year = {2019},
volume = {37},
number = {6},
pages = {866--888},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.1905-m2019-0055},
url = {http://global-sci.org/intro/article_detail/jcm/13380.html}
}