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

Please always quote using this URN: urn:nbn:de:0297-zib-61107
  • SAP's decision support systems for optimized supply network planning rely on mixed-integer programming as the core engine to compute optimal or near-optimal solutions. The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of a robust and future-proof decision support system for a large and diverse customer base. In this paper we describe our coordinated efforts to ensure that the performance of the underlying solution algorithms matches the complexity of the large supply chain problems and tight time limits encountered in practice.

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Metadaten
Author:Gerald Gamrath, Ambros Gleixner, Thorsten Koch, Matthias Miltenberger, Dimitri Kniasew, Dominik Schlögel, Alexander Martin, Dieter Weninger
Document Type:ZIB-Report
Tag:supply chain management, supply network optimization, mixed-integer linear programming, primal heuristics, numerical stability, large-scale optimization
MSC-Classification:90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING / 90Bxx Operations research and management science / 90B06 Transportation, logistics
90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING / 90Cxx Mathematical programming [See also 49Mxx, 65Kxx] / 90C11 Mixed integer programming
Date of first Publication:2016/11/24
Series (Serial Number):ZIB-Report (16-45)
ISSN:1438-0064

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