Optimization of Mixed Control Supply Chain Logistics Planning Under Uncertain Environment

Optimization of Mixed Control Supply Chain Logistics Planning Under Uncertain Environment 

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 elfeclive method which can quantihably 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. 

It is quite common to describe the demand with random variables and fiizzy numbers, but in the complicated market surroundings, it is not accurate enough to describe the demand with a single random variable or fiizzy variable under many circumstances. For example, the future market demand might be good, or moderate, or bad, these three cases have happened in a certain probabihty, that is to say it is stochastic, but the market demand after each possibility is a fuzzy process, for example, when the market is good, the demand might be around 5000 units when the market is moderate, it is around 2600 units. In the bad case, it is only around 900 units. These descriptions are fuzzy, which means the customer demand is a fuzzy random variable. 

Shao et al.. Optimization of Integrated Supply Chain Planning under Multiple Uncertainty, DOI 10.1007/978-3-662-47250-7 6 

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