The stochastic ordering of random variables is extended to the cases where the available data are imprecise quantities, rather than crisp. To do this, using some elements of fuzzy set theory, we suggest the fuzzy reversed hazard rate and fuzzy mean inactivity time functions and apply them to construct some new fuzzy stochastic orders for ranking fuzzy random variables. In addition, we study the...

A fuzzy random model of Combined Location Routing and Inventory Problem (CLRIP) has been presented with continuous review inventory policy in B2C distribution environment for the first time. Demands of customers and distribution centers are uncertain and have been assumed to be fuzzy random variables. To solve the model, first the expected value of fuzzy random variable and the possibilistic me...

This communication is concerned with the problem of supervised classification of fuzzy data obtained from a random experiment. The data generation process is modelled through fuzzy random variables which, from a formal point of view, can be identified with a kind of functional random element. We propose to adapt one of the most versatile discriminant approaches in the context of functional data...

The environment of loan in bank is very complex, there are not only random factors but also fuzzy factors, so the return rates of loan often have fuzzy random characteristic. Mean chance is a measure of fuzzy random variable. This paper proposes two fuzzy random dependentchance programming models of loan portfolio, one is minimize the mean chance of a bad outcome under the certain expected retu...

In this paper we study how to make joint extensions of stochastic orderings and interval orderings so as to extend methods for comparing random variables, from the point of view of their respective location or magnitude, to fuzzy random variables. The main idea is that the way fuzzy random variables are interpreted affects the choice of the comparison methods. We distinguish three views of fuzz...

The purpose of this paper is to describe an adaptive fuzzy cognitive map based on the random neural network model. The adaptive fuzzy cognitive map changes its fuzzy causal web as causal patterns change and as experts update their causal knowledge. We show how the adaptive random fuzzy cognitive map can reveal implications of models composed of dynamic processes.

The formulation of strong and weak laws of large numbers for fuzzy random variables based on variance is given. The concept of the variance of fuzzy random variables, introduced by K1 orner (Fuzzy Sets and Systems 92 (1997) 83) and by Feng (Fuzzy Sets and Systems 120 (2001) 487) demonstrates its e2ciency. c © 2002 Elsevier Science B.V. All rights reserved.

In this communication we will consider hypothesis-tests for the (fuzzy-valued) mean value of a fuzzy random variable in a population. For this purpose, we will make use of a generalized metric for fuzzy numbers, and we will develop two different approaches for the case of fuzzy random variables taking on a finite number of possible values, both leading to close statistical inferences.

In this paper, we simulate different types of random fuzzy variables to get some conclusions concerning fuzzy-valued random variables. This simulation has been carried out to illustrate certain limit results formalizing the convergence of the arithmetic mean of sample fuzzy data to the population mean (or expected value of the random fuzzy variable), like the well-known strong law of large numb...

This paper presents an implementation of the Imperialist Competitive Algorithm (ICA) for solving the fuzzy random portfolio selection problem where the asset returns are represented by fuzzy random variables. Portfolio Optimization is an important research field in modern finance. By using the necessity-based model, fuzzy random variables reformulate to the linear programming and ICA will be de...