TY - JOUR
T1 - Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming
AU - Gamrath , Gerald
AU - Gleixner , Ambros
AU - Koch , Thorsten
AU - Miltenberger , Matthias
AU - Kniasew , Dimitri
AU - Schlögel , Dominik
AU - Martin , Alexander
AU - Weninger , Dieter
JO - Journal of Computational Mathematics
VL - 6
SP - 866
EP - 888
PY - 2019
DA - 2019/11
SN - 37
DO - http://doi.org/10.4208/jcm.1905-m2019-0055
UR - https://global-sci.org/intro/article_detail/jcm/13380.html
KW - Supply chain management, Supply network optimization, Mixed-integer linear programming, Primal heuristics, Numerical stability, Large-scale optimization.
AB - <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>