Advances in stochastic programming and robust optimization for supply chain planning

被引：38

作者：

Govindan, Kannan ^{[1
]}

Cheng, T. C. E. ^{[2
]}

机构：

[1] Univ Southern Denmark, Ctr Sustainable Supply Chain Engn, Dept Technol & Innovat, Odense, Denmark

[2] Hong Kong Polytech Univ, Dept Logist & Maritime Studies, Hong Kong, Hong Kong, Peoples R China

来源：

COMPUTERS & OPERATIONS RESEARCH | 2018年 / 100卷

关键词：

Supply chain planning; Stochastic programming; Robust optimization; Uncertainties; DEMAND UNCERTAINTY; RISK-MANAGEMENT; NETWORK DESIGN; FRAMEWORK; MODELS;

D O I：

10.1016/j.cor.2018.07.027

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This special issue addresses the advances in stochastic programming and robust optimization for supply chain planning by examining novel methods, practices, and opportunities. The articles present and analyze opportunities to improve supply chain planning through exploring various uncertainty situations and problems, sustainability assessment, vendor selection, risk mitigation, retail supply chain planning, and supply chain coordination. This editorial note summarizes the discussions on the stochastic models, algorithms, and methodologies developed for the evaluation and effective implementation of supply chain planning under various concerns. A dominant finding is that supply chain planning through the advancement of stochastic programming and robust optimization should be explored in a variety of ways and within different fields of applications. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：262 / 269

页数：8

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