Year: 2019
Author: Gerald Gamrath, Ambros Gleixner, Thorsten Koch, Matthias Miltenberger, Dimitri Kniasew, Dominik Schlögel, Alexander Martin, Dieter Weninger
Journal of Computational Mathematics, Vol. 37 (2019), Iss. 6 : pp. 866–888
Abstract
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.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.1905-m2019-0055
Journal of Computational Mathematics, Vol. 37 (2019), Iss. 6 : pp. 866–888
Published online: 2019-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 23
Keywords: Supply chain management Supply network optimization Mixed-integer linear programming Primal heuristics Numerical stability Large-scale optimization.