A multi-criteria decision-making method for hesitant fuzzy linguistic term set based on the cloud model and evidence theory

Multicriteria decision-making methods and application on the basis of probabilistic uncertain trapezium cloud

Probabilistic Uncertain Linguistic Term Set (PULTS), as an emerging and effective linguistic expression tool, can appropriately describe the complex evaluation information of decision makers. The cloud model is powerful in handling complex cognitive linguistic information, based on which, this paper proposes two new Multicriteria Decision-Making (MCDM) Methods with PULTSs. Firstly, to avoid the problem of information loss in traditional linguistic conversion methods, Probabilistic Uncertainty Trapezium Cloud (PUTC) is proposed to quantify linguistic evaluation information. Secondly, the Probabilistic Uncertainty Trapezium Cloud Weighted Bonferroni mean (PUTCWBM) operator is defined, while presenting a new cloud score function and similarity measures. Additionally, two ranking methods are proposed, one on the basis of the similarity measures of PUTCs and ideal solutions, the other on the basis of the PUTCWBM operator and the cloud score function. Finally, the two methods are verified with an example of evaluation on masks, and the effectiveness and superiority of the methods are further illustrated through sensitivity analysis and method comparison.

A review on fuzzy preference modeling methods for group decision-making

With the rise of group decision-making and the increasingly complex decision-making environment, preference modeling for decision makers has become more and more important, and many preference modeling methods have emerged. Based on the fuzzy theory, researchers have proposed a large number of preference models to express the subjective uncertainty of decision makers. These methods based on fuzzy theory are collectively referred to as fuzzy preference modeling methods. The fuzzy sets preference model is the first practice of fuzzy theory used in the field of preference modeling, and it is still widely used by researchers until now. Subsequently, based on fuzzy theory, the researchers also proposed linguistic term sets and cloud model. These methods have different representation domains, and are applicable to different decision-making environment. In this paper we give a review of classical fuzzy preference modeling methods and its latest extensions and variants. After the presentation of comparative analyses on the existing methods, we figure out some current challenges and possible future development directions in the field of fuzzy preference modeling.

INVESTMENT DECISION ANALYSIS OF INTERNATIONAL MEGAPROJECTS BASED ON COGNITIVE LINGUISTIC CLOUD MODELS

The investment decision analysis of international megaprojects is a major area of interest. The choice of international megaprojects usually depends on the multi-discipline knowledge from experts. Besides, experts may not be able to provide accurate or crisp evaluations such as deterministic numbers on each criterion because of the complexity of the decision problem. In this case, natural evaluation language, either single linguistic variable or multiple linguistic variables, is a good expression tool for experts to sharing their opinions freely and flexibly. To this end, this paper introduces a cognitive linguistic cloud model for the investment decision analysis of international megaprojects as a decision support system and provides a survey of the cloud model. Afterwards, the technique to tackle multi-granularity of cognitive linguistic information is proposed to capture personalized semantics. In addition, operators of the cognitive linguistic model are proposed to aggregate natural language. The proposed approach has the advantages of more accurate utilization of experts’ knowledge, reducing uncertainties, and more effective operations of cognitive clouds for decision analysis in comparing with the state of the art. Finally, a case study about the investment of international megaprojects is given to show the flexibility and understandability of the cognitive linguistic model.