Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming

Authors

  • Gerald Gamrath Zuse Institute Berlin, Department Optimization
  • Ambros Gleixner Zuse Institute Berlin, Department Optimization
  • Thorsten Koch Zuse Institute Berlin and Technische Universitat Berlin, Germany
  • Matthias Miltenberger Zuse Institute Berlin, Department Optimization
  • Dimitri Kniasew SAP SE
  • Dominik Schlögel SAP SE
  • Alexander Martin Friedrich-Alexander-Universität Erlangen-Nürnberg, Department Mathematics
  • Dieter Weninger Friedrich-Alexander-Universität Erlangen-Nürnberg, Department Mathematics

DOI:

https://doi.org/10.4208/jcm.1905-m2019-0055

Keywords:

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

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.

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Published

2021-07-01

Issue

Vol. 37 No. 6 (2019)

Section

Articles