Incomplete Fuzzy Linguistic Preference Relations Research Articles

Revisiting inconsistent judgments for incomplete fuzzy linguistic preference relations: Algorithms to identify and rectify ordinal inconsistencies

Fuzzy linguistic preference relations (FLPRs), which decision makers (DMs) use to express their pairwise comparison information, are suitable for modeling the qualitative aspects of problems. Ordinal consistency is the minimum condition that DM’s preference values arenot self-contradictory. In this paper, we first define and study the ordinal consistency of the incomplete FLPRs. A novel algorithm is developed to judge whether an incomplete FLPR is ordinally consistent. The proposed algorithm can also identify all cycles of length 3 to n in the digraph of an incomplete FLPR. Based on the proposed algorithm and two rules, another algorithm is proposed to rectify the inconsistency of an incomplete FLPR and improve it so that it is ordinally consistent. Furthermore, the proposed algorithms can also be used to identify and rectify the ordinal inconsistency of the complete FLPRs. Finally, two examples are given, and comparisons with the existing methods are also provided to demonstrate the effectiveness and validity of the proposed methods.

A model based on subjective linguistic preference relations for group decision making problems

In a group decision making problem, experts often express their opinions through the so-called preference relations. In recent years, several different definitions of preference relations depending on the framework and the nature of the problem have been introduced. These approaches vary from interval-valued fuzzy preference relations to incomplete fuzzy linguistic preference relations. In this paper, a novel definition of preference relation, the so-called subjective linguistic preference relation, is proposed. These preference relations are based on the concept of subjective evaluations, introduced in the linguistic computational model based on discrete fuzzy numbers. In this framework, the experts have more flexibility to express their opinions and the solid mathematical background of this model is a guarantee of no loss of information. Finally, an example of a multi-expert decision making problem with a hierarchical multi-granular linguistic context is analyzed to illustrate the potential of the proposed method and its advantages with respect to other methods.

Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries

As in the Web, the growing of information is the main problem of the academic digital libraries. Thus, similar tools could be applied in university digital libraries to facilitate the information access by the students and teachers. In [46] we presented a fuzzy linguistic recommender system to advice research resources in university digital libraries. The problem of this system is that the user profiles are provided directly by the own users and the process for acquiring user preferences is quite difficult because it requires too much user effort. In this paper we present a new fuzzy linguistic recommender system that facilitates the acquisition of the user preferences to characterize the user profiles. We allow users to provide their preferences by means of incomplete fuzzy linguistic preference relation. We include tools to manage incomplete information when the users express their preferences, and, in such a way, we show that the acquisition of the user profiles is improved.

Incomplete fuzzy linguistic preference relations under uncertain environments

When addressing decision-making problems, decision-makers typically express their opinions using fuzzy preference relations. In some instances, decision-makers may have to deal with the problems in which only partial information is available. Consequently, decision-makers embody their preferences as incomplete fuzzy preference relations. The values of incomplete fuzzy preference relations have been considered crisp in recent studies. To allow decision-makers to provide vague or imprecise responses, this study proposes a novel method, called incomplete fuzzy linguistic preference relations, that uses fuzzy linguistic assessment variables instead of crisp values of incomplete fuzzy preference relations to ensure comparison consistency. The proposed method reflects an environment in which some uncertainty or vagueness exists. Examples are included that illustrate the effectiveness of the proposed method.

Group decision making with incomplete fuzzy linguistic preference relations

Group decision making with incomplete fuzzy linguistic preference relations