2,581 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
Hesitant Fuzzy Linguistic Analytic Hierarchical Process With Prioritization, Consistency Checking, and Inconsistency Repairing
Analytic hierarchy process (AHP), as one of the most important methods to tackle multiple
criteria decision-making problems, has achieved much success over the past several decades. Given that
linguistic expressions are much closer than numerical values or single linguistic terms to a human way of
thinking and cognition, this paper investigates the AHP with comparative linguistic expressions. After providing
the snapshot of classical AHP and its fuzzy extensions, we propose the framework of hesitant
fuzzy linguistic AHP, which shows how to yield a decision for qualitative decision-making problems with
complex linguistic expressions. First, the comparative linguistic expressions over criteria or alternatives
are transformed into hesitant fuzzy linguistic elements and then the hesitant fuzzy linguistic preference
relations (HFLPRs) are constructed. Considering that HFLPRs may be inconsistent, we conduct consistency
checking and improving processes after obtaining priorities from the HFLPRs based on a linear programming
method. Regarding the consistency-improving process, we develop a new way to establish a perfectly
consistent HFLPR. The procedure of the hesitant fuzzy linguistic AHP is given in stepwise. Finally,
a numerical example concerning the used-car management in a lemon market is given to illustrate the
ef ciency of the proposed hesitant fuzzy linguistic AHP method.This work was supported in part by the National Natural Science Foundation of China under Grant 71771156, in part by the 2019 Sichuan
Planning Project of Social Science under Grant SC18A007, in part by the 2019 Soft Science Project of Sichuan Science and Technology
Department under Grant 2019JDR0141, and in part by the Project of Innovation at Sichuan University under Grant 2018hhs-43
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
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
Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making
The paper addresses the growing importance of Large Scale Group Decision Making (LSGDM) problems, focusing on hesitant fuzzy LSGDM. It introduces a Reliability Index-based Consensus Reaching Process (RI-CRP) to enhance efficiency. The proposed method assesses the ordinal consistency of decision makers' (DMs) information, measures deviation, and assigns a reliability index to DMs' opinions. An unreliable DMs management method is presented to filter out unreliable information. Additionally, an Alternative Ranking-based Clustering (ARC) method with hesitant fuzzy reciprocal preference relations is proposed to improve the efficiency of RI-CRP. The numerical example demonstrates the feasibility and effectiveness of the ARC method and RI-CRP for hesitant fuzzy LSGDM problems.Este artículo aborda la creciente importancia de los problemas de Toma de Decisiones en Grupo a Gran Escala (LSGDM), centrándose en el LSGDM difuso vacilante. Introduce un Proceso de Consenso Basado en Índices de Fiabilidad (RI-CRP) para mejorar la eficiencia. El método propuesto evalúa la consistencia ordinal de la información de los decisores, mide la desviación y asigna un índice de fiabilidad a las opiniones de los decisores. Se presenta un método de gestión de los decisores poco fiables para filtrar la información poco fiable. Además, se propone un método de agrupamiento alternativo basado en la clasificación (ARC) con relaciones de preferencia recíproca difusas vacilantes para mejorar la eficacia de RI-CRP. El ejemplo numérico demuestra la viabilidad y eficacia del método ARC y del RI-CRP para problemas LSGDM difusos vacilantes.Instituto Interuniversitario de Investigación en Data Science and Computational Intelligence (DaSCI
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
A dynamic feedback mechanism with attitudinal consensus threshold for minimum adjustment cost in group decision making
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 71971135, Grant 71571166, Grant 72071056, and Grant 71910107002, in part by the Innovative Talent Training Project of Graduate Students in Shanghai Maritime University of China under Grant 2019YBR017, and in part by the Spanish State Research Agency under Project PID2019-103880RB-I00/AEI/10.13039/501100011033.This article presents a theoretical framework for a
dynamic feedback mechanism in group decision making (GDM)
by the implementation of an attitudinal consensus threshold
(ACT) to generate recommendation advice for the identified
inconsistent experts with the aim to increase consensus. The
novelty of the approach resides in its ability to implement the
ACT continuously, which allows the covering of all possible
consensus states of the group from its minimum to maximum
consensus degrees. Therefore, it can be flexibly applied to GDM
problems with different consistency requirements. A sensitivity
analysis method with visual simulation is proposed to support
the checking of the numbers of experts involved in the feedback
process and the minimum adjustment cost associated with the
different ACT intervals. Experimental results show that an
increase in the ACT value will lead to an increase in the number
of experts and adjustment cost involved in the feedback process.
Eventually, a numerical example is included to simulate the
feedback process under various decision making scenarios with
different ACT intervals.National Natural Science Foundation of China (NSFC) 71971135
71571166
72071056
71910107002Innovative Talent Training Project of Graduate Students in Shanghai Maritime University of China 2019YBR017Spanish Government PID2019-103880RB-I00/AEI/10.13039/50110001103
Integer programming modeling on group decision making with incomplete hesitant fuzzy linguistic preference relations
© 2013 IEEE. Complementing missing information and priority vector are of significance important aspects in group decision making (GDM) with incomplete hesitant fuzzy linguistic preference relations (HFLPRs). In this paper, an integer programming model is developed based on additive consistency to estimate missing values of incomplete HFLPRs by using additive consistency. Once the missing values are complemented, a mixed 0-1 programming model is established to derive the priority vectors from complete HFLPRs, in which the underlying idea of the mixed 0-1 programming model is the probability sampling in statistics and minimum deviation between the priority vector and HFLPR. In addition, we also propose a new GDM approach for incomplete HFLPRs by integrating the integer programming model and the mixed 0-1 programming model. Finally, two case studies and comparative analysis detail the application of the proposed models
Fuzzy Techniques for Decision Making 2018
Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches
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