Optimization of Mixed Control Supply Chain Logistics Planning Under Uncertain Environment

Juping Shao (), Yanan Sun () and Bernd Noche ()
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Juping Shao: Suzhou University of Science and Technology
Yanan Sun: Suzhou Industrial Park Anwood Logistics System Co., Ltd
Bernd Noche: University Duisburg-Essen

Chapter Chapter 6 in Optimization of Integrated Supply Chain Planning under Multiple Uncertainty, 2015, pp 149-183 from Springer

Abstract: Abstract Usually, the demand is described with random variables under uncertain environment. When we describe the demand with variables, we need a great amount of empirical statistics to get the distribution function. However, these data might be hard to get in some cases. The fussy sets theory is then a commonly used and effective method which can quantifiably describe the uncertain demand. The membership function of fuzzy numbers can be determined by the decision makers when using fuzzy numbers to describe the demand, which is much easier to determine the membership function than the determination of distribution functions of random variable.

Keywords: Supply Chain; Fuzzy Random Variables; Hybrid Intelligent Algorithm; Fuzzy Stochastic; Well-trained Neural Network (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-662-47250-7_6

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