A new class of fuzzy stochastic optimization models—two-stage fuzzy stochastic programming with Value-at-Risk (FSP-VaR) criteria is built in this paper. Some properties of the two-stage FSP-VaR, such as value of perfect information (VPI), value of fuzzy random solution (VFRS), and bounds of the fuzzy random solution, are discussed. An Approximation Algorithm is proposed to compute the VaR by co...

This article addresses the fuzziness in regression models. The goal is to present a test procedure to explicitly examine whether an independent variable has a clear functional relationship with the dependent variable in a specific regression model, or whether their relationship is fuzzy. To this end, we interpret the spread of the regression coefficients as a statistic measuring the fuzziness o...

Type-2 (T2) fuzzy variable is an extension of an ordinary fuzzy variable. In fuzzy possibility theory, T2 fuzzy variable is defined as a measurable map from the universe to the set of real numbers, and the possibility of a T2 fuzzy variable takes on a real number is a regular fuzzy variable (RFV). T2 fuzziness, which is usually used to handle linguistic uncertainties, can be described as T2 fuz...

Product quality strongly depend on production process quality. So, performance of production process must be strictly monitored. Statistical Process Control (SPC) is a technique used for evaluate process performance using statistical method to analyze, control, and influence process performance improvement. Combined of two control chart, individual chart and cusum chart could improve performanc...

Clouds are a concept for uncertainty mediating between the concept of a fuzzy set and that of a probability distribution. A cloud is to a random variable more or less what an interval is to a number. We discuss the basic theoretical and numerical properties of clouds, and relate them to histograms, cumulative distribution functions, and likelihood ratios. We show how to compute nonlinear transf...

In this paper, the portfolio selection problem is considered, where fuzziness and randomness appear simultaneously in optimization process. Since return and dividend play an important role in such problems, a new model is developed in a mixed environment by incorporating fuzzy random variable as multi-objective nonlinear model. Then a novel interactive approach is proposed to determine the pref...

Inventory decisional environment of short life-cycle products is full of uncertainties arising from randomness and fuzziness of input parameters like customer demand requiring modeling under hybrid uncertainty. Prior inventory models incorporating fuzzy demand have unfortunately ignored stochastic variation of demand. This paper determines an unambiguous optimal order quantity from a set of n f...

Mathematical models of physical systems often contain parameters with an imprecisely known and uncertain character. It is quite common to represent these parameters by means of random variables. Numerous methods have been developed to compute accurate approximations to solutions of equations with such parameters. This approach, however, may not be entirely justified when the uncertainty is due ...