Form Of Uncertain Linguistic Variables Research Articles

Model for multiple attribute group decision making with uncertain linguistic information

We investigate the multiple attribute group decision making (MAGDM) problems in which the attribute values take the form of uncertain linguistic variables. Motivated by the idea of power geometric aggregation, in this paper, we develop some uncertain linguistic power geometric aggregation operators: the uncertain linguistic power weighted geometric (ULPWG) operator and the uncertain linguistic power ordered weighted geometric (ULPOWG) operator, and then apply them to multiple attribute group decision making problems with uncertain linguistic information. Finally, an illustrative example for evaluating the urban land intensive use in the Three Gorges reservoir area is given to verify the developed approaches and to demonstrate its practicality and effectiveness.

OWCLCOA Operator and its Application to Comprehensive Evaluating Modeling of Brand Extension in Uncertain Linguistic Setting

With respect to multiple attribute decision making (MADM) problems in which the attribute weights take the form of real numbers, and the preference values take the form of uncertain linguistic variables, a new decision making analysis method is developed. By utilizing some operational laws of linguistic variables, we propose the ordered weighted continuous linguistic choquet ordered averaging (OWCLCOA) operator based on the continuous LOWA operator(C-LOWA) and then develop an OWCLCOA operator-based approach to comprehensive evaluating modeling of brand extension in uncertain linguistic setting. The approach is simple and don’t need the comparison between two uncertain linguistic variables. Finally, an illustrative example with comprehensive evaluating modeling of brand extension is given to verify the developed approach and to demonstrate its practicality and effectiveness.

A method for multiple attribute decision making with incomplete weight information under uncertain linguistic environment

The multi-attribute decision making problems are studied, in which the information about the attribute values take the form of uncertain linguistic variables. The concept of deviation degree between uncertain linguistic variables is defined, and ideal point of uncertain linguistic decision making matrix is also defined. A formula of possibility degree for the comparison between uncertain linguistic variables is proposed. Based on the deviation degree and ideal point of uncertain linguistic variables, an optimization model is established, by solving the model, a simple and exact formula is derived to determine the attribute weights where the information about the attribute weights is completely unknown. For the information about the attribute weights is partly known, another optimization model is established to determine the weights, and then to aggregate the given uncertain linguistic decision information, respectively. A method based on possibility degree is given to rank the alternatives. Finally, an illustrative example is also given.

AN APPROACH TO PURE LINGUISTIC MULTIPLE ATTRIBUTE DECISION MAKING UNDER UNCERTAINTY

The pure linguistic multiple attribute decision making problems are studied, in which the information about the attribute weights are expressed in the form of linguistic variables or uncertain linguistic variables, and the attribute values take the form of uncertain linguistic variables. The operational laws of uncertain linguistic variables are introduced, and two uncertain linguistic aggregation operators called linguistic weighted aggregation operator and uncertain linguistic weighted aggregation operator are developed based on uncertain linguistic variables and their operational laws. An approach based on the developed operators for pure linguistic multiple attribute decision making under uncertainty is proposed. The prominent characteristic of the proposed approach is that it can compute with uncertain linguistic information directly. Furthermore, the approach is straightforward and does not produce any loss of information. Finally, an illustrative example is given.