Uncertain Linguistic Environment Research Articles

An Approach to Potential Evaluation of Emerging Technology Commercialization with Uncertain Linguistic Information

In this paper, we investigate the multiple attribute group decision making (MAGDM) problems for potential evaluation of emerging technology commercialization with uncertain linguistic information. Then, we have developed an uncertain linguistic hybrid harmonic mean (ULHHM) operator. We have proved both ULWHM and ULOWHM operators are the special case of the ULHHM operator. The ULHHM operator generalizes both the ULWHM and ULOWHM operators, and reflects the importance degrees of both the given arguments and their ordered positions. Based on the ULWHM and ULHHM operators, we have proposed an approach to MAGDM for potential evaluation of emerging technology commercialization under uncertain linguistic environment. Finally an illustrative example for potential evaluation of emerging technology commercialization with uncertain linguistic information has been given to show the developed approach and to demonstrate its feasibility and practicality.

Model for Evaluating the Computer Network Security in Uncertain Linguistic Environment

Model for Evaluating the Computer Network Security in Uncertain Linguistic Environment

Linguistic power aggregation operators and their application to multiple attribute group decision making

The aim of this study is to develop new linguistic aggregation operators based on the power-average (PA) operator, such as the linguistic power average (LPA) operator, the linguistic weighted PA operator, and the LPOWA operator. We studied some desired properties of the developed operators, such as idempotency and commutativity. Moreover, we developed two approaches to deal with group decision-making problems under linguistic environments. If the weighting vector of the decision makers was known, we developed an approach based on the linguistic weighted PA operator. On the other hand, if the weighting vector of the decision maker was unknown, we developed a different approach based on the LPOWA operator. We also developed new uncertain linguistic operators under uncertain linguistic environments, such as the ULPA operator, the uncertain linguistic weighted PA operator, and the ULPOWA operator. We extended these approaches, which are based on ULWPA and ULPOWA operators, respectively, to solve group decision-making problems under an uncertain linguistic environment. Finally, a practical example is provided to illustrate the multiple-attribute group decision-making process.

Extension of the DEMATEL method in an uncertain linguistic environment

The decision making trial and evaluation laboratory (DEMATEL) method is a useful tool for analyzing correlations among factors using crisp values. However, the crisp values are inadequate to model real-life situations due to the fuzziness and uncertainty that are frequently involved in judgments of experts. The aim of this paper is to extend the DEMATEL method to an uncertain linguistic environment. In this paper, the correlation information among factors provided by experts is in the form of uncertain linguistic terms. A formula is first presented to transform correlation information from uncertain linguistic terms to trapezoidal fuzzy numbers. Then, we aggregate the transformed correlation information of each expert into group information using the operations of trapezoidal fuzzy numbers. The importance and classification of factors are determined via fuzzy matrix operations. Furthermore, a causal diagram is constructed to vividly show the different roles of factors. Finally, an example is used to illustrate the procedure of the proposed method.

Multi-period multi-attribute group decision-making under linguistic assessments

In fuzzy environments, decision information is more suitable to be expressed in linguistic labels than exact numerical values. Group decision-making with linguistic assessments has received more and more attention over the last decades. Most research on this topic has focused on situations where all the original decision information is provided at the same time and refers to one and same period. However, in many decision areas, such as multi-period investment decision-making, medical diagnosis, personnel dynamic examination, military system efficiency dynamic evaluation, etc., the original decision information is usually collected at different periods and/or refers to different moments in time. This paper investigates the multi-period multi-attribute group decision-making problems where all decision information is expressed by decision-makers in multiplicative linguistic labels at different periods. The paper first introduces a new operator called a dynamic linguistic weighted geometric (DLWG) operator and uses the minimum variability model to derive the time series weights associated with the DLWG operator, and then utilises, respectively, the linguistic weighted geometric (LWG) operator and the DLWG operator to aggregate the given linguistic labels. Moreover, the paper develops an approach to multi-period multiple attribute group decision-making under linguistic assessments so as to derive the final ranking of alternatives, and finally, gives an illustrative example and extends the above results to uncertain linguistic environments.

CORRELATED LINGUISTIC INFORMATION AGGREGATION

Linguistic information aggregation has received great attention from researchers, and a variety of operators have been developed for aggregating linguistic information. All the existing linguistic information aggregation operators only consider the situations where all the aggregated linguistic arguments are independent, i.e., they only consider the addition of the importance of individual linguistic arguments, however, in some actual situations, the considered linguistic arguments may be correlative. In this paper, we focus on this issue. Motivated by the idea of the well-known Choquet integrals,1 we propose two new linguistic information aggregation operators called the linguistic correlated averaging operator and linguistic correlated geometric operator. In the special cases where the aggregated linguistic arguments are independent, the linguistic correlated averaging operator can be reduced to a variety of traditional linguistic averaging aggregation operators; while the linguistic correlated geometric operator can be reduced to a variety of the traditional linguistic geometric aggregation operators. Furthermore, we extend the above results to accommodate uncertain linguistic environments, and illustrate them with a practical problem.

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.

Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment

In this paper, two uncertain linguistic aggregation operators called uncertain linguistic ordered weighted averaging (ULOWA) operator and uncertain linguistic hybrid aggregation (ULHA) operator are proposed. An approach to multiple attribute group decision making with uncertain linguistic information is developed based on the ULOWA and the ULHA operators. Finally, a practical application of the developed approach to the problem of evaluating university faculty for tenure and promotion is given.