TACKLING INDUSTRIAL-SCALE SUPPLY CHAIN PROBLEMS BY MIXED-INTEGER PROGRAMMING

被引：2

作者：

Gartirath, Gerald ^{[1
]}

Gleixner, Ambros ^{[1
]}

Koch, Thorsten ^{[1
]}

Miltenberger, Matthias ^{[1
]}

Kniasew, Ditnitri ^{[2
]}

Schloegel, Doininik ^{[2
]}

Martin, Alexander ^{[3
]}

Weriiriger, Dieter ^{[3
]}

机构：

[1] Zuse Inst Berlin, Dept Optimizat, Berlin, Germany

[2] SAP Optimizat, SAP SE, Walldorf, Germany

[3] Friedrich Alexander Univ Erlangen Nurnberg, Dept Math, Erlangen, Germany

来源：

JOURNAL OF COMPUTATIONAL MATHEMATICS | 2019年 / 37卷 / 06期

关键词：

Supply chain management; Supply network optimization; Mixed-integer linear programming; Primal heuristics; Numerical stability; Large-scale optimization; SIMPLEX-METHOD; PROGRESS;

D O I：

10.4208/jcm.1905-m2019-0055

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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.

引用

页码：866 / 888

页数：23

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