Volume 37, Issue 6
Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming

Gerald Gamrath, Ambros Gleixner, Thorsten Koch, Matthias Miltenberger, Dimitri Kniasew, Dominik Schlögel, Alexander Martin & Dieter Weninger

J. Comp. Math., 37 (2019), pp. 866-888.

Published online: 2019-11

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  • 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.

  • Keywords

Supply chain management, Supply network optimization, Mixed-integer linear programming, Primal heuristics, Numerical stability, Large-scale optimization.

  • AMS Subject Headings

90B06, 90C05, 90C06, 90C11, 90C90

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address

gamrath@zib.de (Gerald Gamrath)

gleixner@zib.de (Ambros Gleixner)

koch@zib.de (Thorsten Koch)

miltenberger@zib.de (Matthias Miltenberger)

dimitri.kniasew@sap.com (Dimitri Kniasew)

dominik.schloegel@sap.com (Dominik Schlögel)

alexander.martin@math.uni-erlangen.de (Alexander Martin)

dieter.weninger@math.uni-erlangen.de (Dieter Weninger)

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@Article{JCM-37-866, author = {Gamrath , Gerald and Gleixner , Ambros and Koch , Thorsten and Miltenberger , Matthias and Kniasew , Dimitri and Schlögel , Dominik and Martin , 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 = {

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.

}, issn = {1991-7139}, doi = {https://doi.org/10.4208/jcm.1905-m2019-0055}, url = {http://global-sci.org/intro/article_detail/jcm/13380.html} }
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