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

Gerald Gamrath(gamrath***at***zib.de)
Ambros Gleixner(gleixner***at***zib.de)
Thorsten Koch(koch***at***zib.de)
Matthias Miltenberger(miltenberger***at***zib.de)
Dimitri Kniasew(dimitri.kniasew***at***sap.com)
Dominik Schlögel(dominik.schloegel***at***sap.com)
Alexander Martin(alexander.martin***at***math.uni-erlangen.de)
Dieter Weninger(dieter.weninger***at***math.uni-erlangen.de)

Abstract: 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.

Keywords: supply chain management, supply network optimization, mixed-integer linear programming, primal heuristics, numerical stability, large-scale optimization

Category 1: Applications -- OR and Management Sciences (Supply Chain Management )

Category 2: Integer Programming ((Mixed) Integer Linear Programming )

Citation: ZIB-Report 16-45, Zuse Institute Berlin, Takustr. 7, 14195 Berlin, November 2016

Download: [PDF]

Entry Submitted: 11/24/2016
Entry Accepted: 11/24/2016
Entry Last Modified: 11/24/2016

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