4,456 research outputs found
An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The reciprocal preference relation (RPR) is a powerful tool to represent decision makers’ preferences in decision making problems. In recent years, various types of RPRs have been reported and investigated, some of them being the ‘classical’ RPRs, interval-valued RPRs and hesitant RPRs. Additive consistency is one of the most commonly used property to measure the consistency of RPRs, with many methods developed to manage additive consistency of RPRs. To provide a clear perspective on additive consistency issues of RPRs, this paper reviews the consistency measurements of the different types of RPRs. Then, consistency-driven decision making and information fusion methods are also reviewed and classified into four main types: consistency improving methods; consistency-based methods to manage incomplete RPRs; consistency control in consensus decision making methods; and consistency-driven linguistic decision making methods. Finally, with respect to insights gained from prior researches, further directions for the research are proposed
Risk assessment in project management by a graphtheory- based group decision making method with comprehensive linguistic preference information
Risk assessment is a vital part in project management. It is possible
that experts may provide comprehensive linguistic preference
information in distinct forms with respect to different
aspects of the risk assessment problem in investment management.
It is a challenge to model and deal with comprehensive linguistic
preference assessments in multiple forms given by experts.
In this regard, this paper defines the generalised probabilistic linguistic
preference relation (GPLPR) to represent different forms of
linguistic preference information in a unified structure. Then, a
probability cutting method is proposed to simplify the representation
of a GPLPR. Afterwards, a graph-theory-based method is
developed to improve the consistency degree of a GPLPR. A
group decision making method with GPLPRs is then proposed to
carry on the risk assessment in project management. Discussions
regarding the comparative analysis and managerial insights
are given
Managing Incomplete Preference Relations in Decision Making: A Review and Future Trends
In decision making, situations where all experts are able to efficiently express their preferences over all the available options are the exception rather than the rule. Indeed, the above scenario requires all experts to possess a precise or sufficient level of knowledge of the whole problem to tackle, including the ability to discriminate the degree up to which some options are better than others. These assumptions can be seen unrealistic in many decision making situations, especially those involving a large number of alternatives to choose from and/or conflicting and dynamic sources of information. Some methodologies widely adopted in these situations are to discard or to rate more negatively those experts that provide preferences with missing values. However, incomplete information is not equivalent to low quality information, and consequently these methodologies could lead to biased or even bad solutions since useful information might not being taken properly into account in the decision process. Therefore, alternative approaches to manage incomplete preference relations that estimates the missing information in decision making are desirable and possible. This paper presents and analyses methods and processes developed on this area towards the estimation of missing preferences in decision making, and highlights some areas for future research
Risk assessment in project management by a graph-theory-based group decision making method with comprehensive linguistic preference information
The work was supported by the National Natural Science Foundation of China (71971145, 71771156, 72171158), the Andalusian Government under Project P20-00673, and also by the Spanish State Research Agency under Project PID2019-103880RB-I00/AEI/10.13039/501100011033.Risk assessment is a vital part in project management. It is possible
that experts may provide comprehensive linguistic preference
information in distinct forms with respect to different
aspects of the risk assessment problem in investment management.
It is a challenge to model and deal with comprehensive linguistic
preference assessments in multiple forms given by experts.
In this regard, this paper defines the generalised probabilistic linguistic
preference relation (GPLPR) to represent different forms of
linguistic preference information in a unified structure. Then, a
probability cutting method is proposed to simplify the representation
of a GPLPR. Afterwards, a graph-theory-based method is
developed to improve the consistency degree of a GPLPR. A
group decision making method with GPLPRs is then proposed to
carry on the risk assessment in project management. Discussions
regarding the comparative analysis and managerial insights
are given.National Natural Science Foundation of China (NSFC) 71971145
71771156
72171158Andalusian Government P20-00673Spanish Government PID2019-103880RB-I00/AEI/10.13039/50110001103
Consistency based completion approaches of incomplete preference relations in uncertain decision contexts.
Uncertainty, hesitation and vagueness are inherent to human beings when articulating opinions and preferences. Therefore in decision making situations it might well be the case that experts are unable to express their opinions in an accurate way. Under these circumstances, various families of preference relations (PRs) have been proposed (linguistic, intuitionistic and interval fuzzy PRs) to allow the experts to manifest some degree of hesitation when enunciating their opinions. An extreme case of uncertainty happens when an expert is unable to differentiate the degree up to which one preference is preferred to another. Henceforth, incomplete preference relations are possible. It is worth to bear in mind that incomplete information does not mean low quality information, on the contrary, in many occasions experts might prefer no to provide information in other to keep consistency. Consequently mechanism to deal with incomplete information in decision making are necessary. This contribution presents the main consistency based completion approaches to
estimate incomplete preference values in linguistic, intuitionistic and interval fuzzy PRs
A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme
Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of experts’ weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of experts’ weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of experts’ weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions.The first author acknowledges support from the Spanish Ministry of Universities [grant number FPU18/01471]. The second and third author wish to recognize their support from the Serra Hunter program. Finally, this work was supported by the Catalan agency AGAUR through its research group support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/ 501100011033.Peer ReviewedPostprint (published version
Interval Consistency Repairing Method for Double Hierarchy Hesitant Fuzzy Linguistic Preference Relation and Application in the Diagnosis of Lung Cancer
Natural language is more in line with the real thoughts of people
than crisp numbers considering that qualitative language information
is more consistent with the expression habits of experts.
Double hierarchy hesitant fuzzy linguistic preference relation
(DHHFLPR) can be used to express complex linguistic preference
information accurately because the pairwise comparison methods
are more accurate than non-pairwise methods. Consistency
reflects the rationalization of a preference relation and can be
used to judge whether a preference relation is self-contradictory
or not. In this paper, an interval consistency index of DHHFLPR is
developed, which is consisted by the consistency indices of all
double hierarchy linguistic preference relations associated with
the DHHFLPR. Additionally, an average consistency index of
DHHFLPR is given by calculating the average value of the consistency
indices of all double hierarchy linguistic preference relations.
Moreover, we develop a consistency checking and repairing
method for DHHFLPR. Finally, we apply the proposed method
into a practical group decision-making problem that is to identify
the most critical factors in developing lung cancer, and some
comparative analyses involving the connections and differences
among the proposed consistency indices are analysed
Granular computing and optimization model-based method for large-scale group decision-making and its application
In large-scale group decision-making process, some decision makers hesitate among several linguistic terms and cannot compare
some alternatives, so they often express evaluation information
with incomplete hesitant fuzzy linguistic preference relations.
How to obtain suitable large-scale group decision-making results
from incomplete preference information is an important and
interesting issue to concern about. After analyzing the existing
researches, we find that: i) the premise that complete preference
relation is perfectly consistent is too strict, ii) deleting all incomplete linguistic preference relations that cannot be fully completed will lose valid assessment information, iii) semantics given
by decision makers are greatly possible to be changed during the
consistency improving process. In order to solve these issues, this
work proposes a novel method based on Granular computing
and optimization model for large-scale group decision-making,
considering the original consistency of incomplete hesitant fuzzy
linguistic preference relation and improving its consistency without changing semantics during the completion process. An illustrative example and simulation experiments demonstrate the
rationality and advantages of the proposed method: i) semantics
are not changed during the consistency improving process, ii)
completion process does not significantly alter the inherent quality of information, iii) complete preference relations are globally
consistent, iv) final large-scale group decision-making result is
acquired by fusing complete preference relations with different weights
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