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Gerald Gamrath1, Ambros Gleixner1, Thorsten Koch1, Matthias Miltenberger1, Dimitri Kniasew2, Dominik Schlögel2, Alexander Martin3, Dieter Weninger3   

  1. 1 Zuse Institute Berlin, Department Optimization;
    2 SAP SE, SAP Optimization;
    3 Friedrich-Alexander-Universität Erlangen-Nürnberg, Department Mathematics
  • Received:2019-02-26 Revised:2019-05-05 Online:2019-12-15 Published:2019-12-15

Gerald Gamrath, Ambros Gleixner, Thorsten Koch, Matthias Miltenberger, Dimitri Kniasew, Dominik Schlögel, Alexander Martin, Dieter Weninger. TACKLING INDUSTRIAL-SCALE SUPPLY CHAIN PROBLEMS BY MIXED-INTEGER PROGRAMMING[J]. Journal of Computational Mathematics, 2019, 37(6): 866-888.

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.

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