Abstract
When supply chain networks become more complex through the application of modern trends such as outsourcing and global marketing, supply chains become more uncertain. Supply chain planning under uncertainty is a challenge for decision makers. Without considering uncertainties in supply chain planning, global supply chains may suffer enormous economic costs. When probability distributions for uncertain parameters can be estimated, stochastic programming can be used for capturing the characteristics of uncertainties and generating flexible production and transportation plans for global supply chains. This paper presents an outline on how to use stochastic programming for decision support under uncertainty. This includes a high level exposition of how to quantify uncertainties, develop stochastic programming models, generate representative scenarios, apply algorithms for model solving, undertake experimental design and present computational results. Through exemplifying supply chain planning and decision making under uncertainty by using stochastic programming, this paper aims to provide a valuable reference for future research in this area.
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