Group decision making with incomplete fuzzy linguistic preference relations (original) (raw)
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Group decision making with compatibility measures of hesitant fuzzy linguistic preference relations
Soft Computing, 2017
Compatibility measure is a very well-known method to deal with decision making problems with uncertain preference relations. In this paper, on the basis of generalized distance measure of hesitant fuzzy linguistic elements, we define the compatibility measures for hesitant fuzzy linguistic preference relations (HFLPRs), including compatibility degree, compatibility index, and acceptable compatibility, and some important special properties are also discussed. Furthermore, based on the compatibility measures of HFLPRs, we propose a method for determining the experts' weights in group decision making (GDM), as well as establish an algorithm for solving the GDM problem with HFLPRs. A case study is set up to utilize the compatibility measures of HFLPRs for dealing with a practical GDM problem, which is to determine which aspect is optimal in the process of medical and health system reform. Finally, some comparisons and analyses are also discussed in detail. Keywords Hesitant fuzzy linguistic preference relations • Compatibility measure • Weights-determining method • Group decision making • Medical and health system reform Communicated by V. Loia.
Information Sciences, 2019
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights We define double hierarchy hesitant fuzzy linguistic preference relation (DHHFLPR) A novel method is developed to calculate the consistency thresholds Two consistency repairing algorithms is proposed to improve the DHHFLPR A method is set up to deal with GDM problems with DHHFLPRs The proposed method is validated by a practical group decision making problem
On group decision making under linguistic preferences and fuzzy linguistic quantifiers
1995
ABSTRACT In this paper some results on group decision making under linguistic preferences and fuzzy linguistic quantifiers are presented. Assuming a set of individual linguistic preferences, representing the preferences of the particular individuals, we develop a solution method for the choice process. We define a linguistic ordered weighted averaging operator, and use it for deriving a collective linguistic preference where the weights are defined using a fuzzy linguistic quantifier.
2014
The experts may have difficulty in expressing all their preferences over alternatives or criteria, and produce the incomplete linguistic preference relation. Consistency plays an important role in estimating unknown values from an incomplete linguistic preference relation. Many methods have been developed to obtain a complete linguistic preference relation based on additive consistency, but some unreasonable values may be produced in the estimation process. To overcome this issue, we propose a new characterisation about multiplicative consistency of the linguistic preference relation, present an algorithm to estimate missing values from an incomplete linguistic preference relation, and establish a decision support system for aiding the experts to complete their linguistic preference relations in a more consistent way. Some examples are also given to illustrate the proposed methods.
A linguistic decision process in group decision making
Group Decision and Negotiation, 1996
Assuming a set of linguistic preferences representing the preferences of the individuals, a linguistic choice process is presented. This is developed using the concept of fuzzy majority for deriving a collective linguistic preference, and the concept of nondominated alternatives for deriving the selected alternatives in the linguistic choice process. The fuzzy majorities are equated with fuzzy linguistic quantifiers. The collective linguistic preference is derived by means of a linguistic ordered weighted averaging operator whose weights are defined using a fuzzy linguistic quantifier. In order to obtain the nondominated alternatives, we present a novel reformulation of Orlovski's nondominance degree under linguistic information.
In group decision making situations, there may be cases in which experts do not have an in-depth knowledge of the problem to be solved and, as a result, they may present incomplete information. In this paper, we present a new selection process to deal with incomplete fuzzy linguistic information. As part of it, we use an iterative procedure to estimate the missing information. This procedure is guided by the additive consistency property and only uses the preference values provided by the experts. In addition, the additive consistency property is also used to measure the level of consistency of the information provided by the experts. The main novelties of this selection process are both the possibility to manage decision situations under incomplete fuzzy linguistic information and the importance of the experts' preferences in the aggregation processes is modeled by means of the experts' consistency.
Group decision-making model with incomplete fuzzy preference relations based on additive consistency
2007
Abstract In decision-making problems there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem, and as a result they may present incomplete preferences, ie, some preference values may not be given or may be missing. In this paper, we present a new model for group decision making in which experts' preferences can be expressed as incomplete fuzzy preference relations.
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A Consensus Model for Group Decision Making With Incomplete Fuzzy Preference Relations
IEEE Transactions on Fuzzy Systems, 2000
We present a consensus model for group decision making with unbalanced fuzzy linguistic preference relations, i.e., assuming that the preferences are assessed on linguistic term sets whose terms are not symmetrically and uniformly distributed. This consensus model can manage incomplete information situations, that is, situations where the experts do not give all the preference values that they are usually requested. In addition, both consistency and consensus measures are used and it allows to achieve consistent solutions with a great level of agreement.