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

作     者：Gerald Gamrath Ambros Gleixner Thorsten Koch Matt hias Miltenberger Dimitri Kniasew Dominik Schlogel Alexander Martin Dieter Weninger 

作者机构：Zuse Institute BerlinDepartment Optimization SAP SESAP Optimization Friedrich-Alexander-Universitat Erlangen-NiimbeTgDepartment Mathematics 

出 版 物：《Journal of Computational Mathematics》 (计算数学（英文）)

年 卷 期：2019年第37卷第6期

页      面：866-888页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070105[理学-运筹学与控制论] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Research Campus Modal Mathematical Optimization Data Analysis Laboratories funded by the Federal Ministry of Education and Research Furthermore, we acknowledge funding through the DFG SFB/Transregio 154 

主　　题：Supply chain management Supply network optimization Mixed-integer linear programming Primal heuristics Numerical stability Large-scale optimization 

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 *** complexity of industrial-scale supply chain optimization,however,often poses limits to the application of general mixed-integer programming *** 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 *** computational evaluation is based on a diverse set,modeling real-world scenarios supplied by our industry partner SAP.