1,909 research outputs found
A contribution to consensus modeling in decision-making by means of linguistic assessments
Decision-making is an active field of research. Specifically, in recent times, a lot of contributions have been presented on decision-making under linguistic assessments. To tackle this kind of processes, hesitant fuzzy linguistic term sets have been introduced to grasp the uncertainty inherent in human reasoning when expressing preferences. This thesis introduces an extension of the set of hesitant fuzzy linguistic term sets to capture differences between non-compatible assessments. Based on this extension, a distance between linguistic assessments is defined to quantify differences between several opinions. This distance is used in turn to present a representative opinion from a group in a decision-making process. In addition, different consensus measures are introduced to determine the level of agreement or disagreement within a decision-making group and are used to define a decision maker’s profile to keep track of their dissension with respect to the group as well as their level of hesitancy. Furthermore, with the aim of allowing decision makers to choose the linguistic terms that they feel more comfortable with, the concept of free double hierarchy hesitant fuzzy linguistic term set is developed in this thesis. Finally, a new approach of the TOPSIS methodology for processes in which the assessments are given by means of free double hierarchy hesitant fuzzy information is presented to rank alternatives under these circumstances.Postprint (published version
The risk assessment of construction project investment based on prospect theory with linguistic preference orderings
Multiple experts decision-making (MEDM) can be regarded as a
situation where a group of experts are invited to provide their
opinions by evaluating the given alternatives, and then select the
optimal alternative(s). As a useful linguistic expression model, linguistic
preference orderings (LPOs) were established in which the
order of alternatives and the relationships between two adjacent
alternatives are fused well. Considering that prospect theory has
the superiority in depicting risk attitudes (risk seeking for losses
and risk aversion for gains) during the uncertain decision-making
process, this paper develops a consensus model based on prospect
theory to deal with MEDM problems with LPOs. Firstly, each
LPO provided by expert is transformed into the responding
DHLPR with complete consistency. Then, the reference point of
expert is determined and the prospect preference matrix is established.
Moreover, we can obtain the overall prospect consensus
degree for a MEDM problem by calculating the similarity degree
between individual and collective prospect preference matrix.
Furthermore, a consensus improvement method is developed to
complete the consensus reaching process. Finally, we apply the
proposed method to deal with a practical MEDM problem involving
the construction project investment, and make some comparative
analyses with existing methods.National Natural Science Foundation of China (NSFC)
71771155China Postdoctoral Science Foundation
2020M680151Sichuan Postdoctoral Science special FoundationSichuan University Postdoctoral Interdisciplinary Innovation Startup FoundationFundamental Research Funds for the Central Universities
YJ202015European Union (EU)
TIN2016-75850-RSichuan Province System Science and Enterprise Development Research Center
Xq20B0
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
Combined probabilistic linguistic term set and ELECTRE II method for solving a venture capital project evaluation problem
Multiple criteria decision making (MCDM) frameworks assist people in assessing alternatives and making reasonable decisions,
with the ELECTRE II MCDM method in particular being widely
applied to many diverse fields. As it is not always possible to
assess qualitative attributes or accurately evaluate alternatives
using precise values, this paper proposes a new approach that combines the ELECTRE II method with probabilistic linguistic term sets
(PLTS) to allow decision makers to state their qualitative preferences
using corresponding probabilities. To demonstrate the viability of
the PTLS-ELECTRE II method and assess its practicability, the proposed method was applied to a typical MCDM venture capital project evaluation problem, for which a comprehensive venture capital
project evaluation index system was constructed that included multiple qualitative and quantitative indicators, such as industry background, marketing, product technology, team management and
financial data. The reasonable evaluation sequence of alternatives
was then determined using the PTLS-ELECTRE II method which can
provide more accurate MCDM decisions
A multidimensional decision with nested probabilistic linguistic term sets and its application in corporate investment
With the rapid development of information, decision making
problems in various fields have presented multidimensional, complex and uncertain characteristics. Nested probabilistic-numerical
linguistic term set (NPNLTS) is an effective tool to describe complex information due to the nested structure and diverse variables. This paper extends the concept of NPNLTS, and defines an
improved form, i.e., nested probabilistic linguistic term set
(NPLTS), and then proposes a novel VIKOR method with nested
probabilistic linguistic information to solve the model. Within the
context of empirical corporate finance, a case study related to
corporate investment decision is presented and handled by the
novel VIKOR method. After that, comparative analysis is carried
out considering other decision-making methods, decision coefficient in VIKOR, and weights of attributes. As a result, the proposed method not only provides a rational and effective solution,
but also reveals the rule in the case when decision coefficient
and weights of attributes change, respectively. Finally, we discuss
the proposed method from the theoretical and application
aspects with a view to guiding future research. To a certain
extent, this study provides a new decision environment to deal
with multidimensional problems
An overview of fuzzy multi-criteria decisionmaking methods in hospitality and tourism industries: bibliometrics, methodologies, applications and future directions
Stakeholders in hospitality and tourism industries are involved in
many decision-making scenarios. Multi-criteria decision-making
(MCDM) methods have been widely used in hospitality and tourism
industries. Although some articles summarised the applications of
MCDM models in hospitality and tourism industries, they ignored the
fuzziness of individual cognition in an uncertain environment. In addition,
these surveys lacked a comprehensive overview from the perspective
of bibliometrics analysis and content analysis regarding the
whole hospitality and tourism industries. To analyse the applications
of fuzzy MCDM methods in hospitality and tourism industries and
further explore future research directions, this article reviews 85
selected papers published from 1997 to 2022 regarding fuzzy MCDM
models applied in hospitality and tourism industries. Through analysing
the results of bibliometric analysis, methodologies and applications,
we found that analytic hierarchy process (AHP) and TOPSIS
methods are the most widely used MCDM methods, and tourism
evaluation, hotel evaluation and selection, tourism destination evaluation
and selection are the most attractive research issues in hospitality
and tourism industries. Finally, future research directions are
proposed from three aspects. This article provides insights for
researchers and practitioners who have interest in fuzzy MCDM models
in hospitality and tourism industries
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
A contribution to consensus modeling in decision-making by means of linguistic assessments
Decision-making is an active field of research. Specifically, in recent times, a lot of contributions have been presented on decision-making under linguistic assessments. To tackle this kind of processes, hesitant fuzzy linguistic term sets have been introduced to grasp the uncertainty inherent in human reasoning when expressing preferences. This thesis introduces an extension of the set of hesitant fuzzy linguistic term sets to capture differences between non-compatible assessments. Based on this extension, a distance between linguistic assessments is defined to quantify differences between several opinions. This distance is used in turn to present a representative opinion from a group in a decision-making process. In addition, different consensus measures are introduced to determine the level of agreement or disagreement within a decision-making group and are used to define a decision maker’s profile to keep track of their dissension with respect to the group as well as their level of hesitancy. Furthermore, with the aim of allowing decision makers to choose the linguistic terms that they feel more comfortable with, the concept of free double hierarchy hesitant fuzzy linguistic term set is developed in this thesis. Finally, a new approach of the TOPSIS methodology for processes in which the assessments are given by means of free double hierarchy hesitant fuzzy information is presented to rank alternatives under these circumstances
A double interaction-based financing group decisionmaking framework considering uncertain information and inconsistent assessment
Financing group decision-making (FGDM), which is an important
stage of project financing, has unique characteristics: large investments
and long payback horizons. Its evaluation results are likely
to be distorted if we ignore the uncertain information and inconsistent
assessment during the decision-making process. In this
study, we propose a double interaction-based FGDM framework
under uncertain information and inconsistent assessment. We
modify the weight setting of evidence reasoning and aggregation
method of probabilistic linguistic term sets to process the above
two issues. The proposed framework is applied in a detailed case
study analysis to display its effectiveness and stability. We expect
the double interaction-based group decision-making framework
under uncertain information and inconsistent assessment to be a
useful tool to understand FGDM processes
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