121 research outputs found
Pension service institution selection by a personalized quantifier-based MACONT method
With the emergence of a variety of pension service institutions, how to choose a suitable institution has become a strategic decision-making problem faced by pension service demanders. To solve this problem, this study identifies key evaluation criteria of pension service institutions through the analysis of the relevant literature. Then, this study proposes a mixed aggregation by comprehensive normalization technique (MACONT) with a personalized quantifier to select pension service institutions, where the personalized qualifier with cubic spline interpolation is used to derive the position weights of criteria, and the MACONT is improved to determine the ranking of alternatives. A case study about the selection of pension service institutions is provided to verify the feasibility of the proposed model. It is found that the proposed method is effective in dealing with heterogeneous evaluation information, and the personalized quantifiers can be combined with MACONT methods to obtain an optimal solution associated with the attitude of pension service demanders. The identified key evaluation criteria are not only significant for pension service demanders, but also conducive to the further improvement of property management related to pension services
Multi-criteria group decision making with a partialranking-based ordinal consensus reaching process for automotive development management
The consensus reaching process (CRP) aims at reconciling the
conflicts between individual preferences when eliciting collective
preferences. The ordinal CRP based on the positional orders of
alternatives in linear rankings is straightforward and robust; however, for partial rankings involving preference, indifference and
incomparability relations, there is no explicit positional order but
are binary relations. This study focuses on partial rankings that
may occur when using the ORESTE (organısation, rangement et
Synthese de donnees relarionnelles, in French) method for making
decisions, and designs an ordinal CRP pertaining to the binary
relations of alternatives. Concretely, we propose an enhanced
ordinal consensus measure with two hierarchies to measure the
agreement levels between individual partial rankings. Consensus
degrees are calculated based on the frequency distribution of binary relation types, which can avoid subjective axiomatic assumptions on the relations themselves. Besides, a consensus threshold
determination method close to cognitive expression is developed.
A feedback mechanism is designed to aid experts to modify preferences towards group consensus. An example about the evaluation of automotive design schemes is presented to validate the
proposed ordinal CRP. A ranking result that allows the incomparability relations of design schemes is obtained after the information exchange among experts
Dynamic preference elicitation of customer behaviours in e-commerce from online reviews based on expectation confirmation theory
Preference change, also known as preference drift, is one of the factors
that online retailers need to consider to accurately collect consumer
preferences and make personalised recommendations. Online
reviews have been widely used to analyse the preference drift of
consumers. However, previous studies on online reviews ignored the
psychological perceptions of consumers in terms of satisfaction. This
paper aims to develop a method for dynamic preference elicitation
from online reviews based on exploring the theory of consumer satisfaction
formation. Based on the framework of expectation confirmation
theory, we develop formulas for expressing the relations
among expectation, perceived performance, confirmation, and satisfaction.
We then use the proposed dynamic preference elicitation
model to predict the change of consumer overall preference after
each review and rank products for consumers’ next purchase. We
test the proposed approach with a case study based on a data set
from Amazon.com. It is founded that the satisfaction changes in
each purchase, and this change will affect the prediction of the next
product ranking. The case study is based on one product group, and
further research is needed to see if the operation of the proposed
method can be extended to other kinds of product
Isomorphic multiplicative transitivity for intuitionistic and interval-valued fuzzy preference relations and its application in deriving their priority vectors
Intuitionistic fuzzy preference relations (IFPRs) are used to deal with hesitation while interval-valued fuzzy preference relations (IVFPRs) are for uncertainty in multi-criteria decision making (MCDM). This article aims to explore the isomorphic multiplicative transitivity for IFPRs and IVFPRs, which builds the substantial relationship between hesitation and uncertainty in MCDM. To do that, the definition of the multiplicative transitivity property of IFPRs is established by combining the multiplication of intuitionistic fuzzy sets and Tanino's multiplicative transitivity property of fuzzy preference relations (FPRs). It is proved to be isomorphic to the multiplicative transitivity of IVFPRs derived via Zadeh's Extension Principle. The use of the multiplicative transitivity isomorphism is twofold: (1) to discover the substantial relationship between IFPRs and IVFPRs, which will bridge the gap between hesitation and uncertainty in MCDM problems; and (2) to strengthen the soundness of the multiplicative transitivity property of IFPRs and IVFPRs by supporting each other with two different reliable sources, respectively. Furthermore, based on the existing isomorphism, the concept of multiplicative consistency for IFPRs is defined through a strict mathematical process, and it is proved to satisfy the following several desirable properties: weak--transitivity, max-max--transitivity, and center-division--transitivity. A multiplicative consistency based multi-objective programming (MOP) model is investigated to derive the priority vector from an IFPR. This model has the advantage of not losing information as the priority vector representation coincides with that of the input information, which was not the case with existing methods where crisp priority vectors were derived as a consequence of modelling transitivity just for the intuitionistic membership function and not for the intuitionistic non-membership function. Finally, a numerical example concerning green supply selection is given to validate the efficiency and practicality of the proposed multiplicative consistency MOP model
Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey
Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science
Learning consumer preferences from online textual reviews and ratings based on the aggregation-disaggregation paradigm with attitudinal Choquet integral
Online reviews contain a wealth of information about customers’ concerns
and sentiments. Sentiment analysis can mine consumer preferences
and satisfaction over products/services. Most existing studies on
sentiment analysis only considered how to extract attribute types or
attribute values of products/services from textual reviews, but ignored
the role of attribute-level ratings in reflecting consumer preferences
and satisfaction. Based on sentiment analysis and preference disaggregation,
this paper unifies the quantitative and qualitative information
extracted from attribute-level ratings and textual reviews, respectively,
to obtain attribute types and attribute values of products/services. To
acquire individual consumer preferences concerning product/service
attributes, this paper proposes a method within an aggregation-disaggregation
paradigm based on the attitudinal Choquet integral to
transform overall online ratings into the form of pairwise comparisons.
Compared with the additive value function used in most studies, more
consumer preferences in terms of the importance of attributes, the
interactions between pairwise attributes, and the tolerance of consumers
to make compensation between attribute values in the aggregation
process can be deduced by our proposed method. Several real
cases on TripAdvisor.com are given to show the applicability of the
proposed method
Interval-valued 2-tuple hesitant fuzzy linguistic term set and its application in multiple attribute decision making
[EN] The hesitant fuzzy linguistic term sets can retain the completeness of linguistic information elicitation by assigning a set of possible linguistic terms to a qualitative variable. However, sometimes experts cannot make sure that the objects attain these possible linguistic terms but only provide the degrees of confidence to express their hesitant cognition. Given that the interval numbers can denote the possible membership degrees that an object belongs to a set, it is suitable and convenient to provide an interval-valued index to measure the degree of a linguistic variable to a given hesitant fuzzy linguistic term set. Inspired by this idea, we introduce the concept of interval-valued 2-tuple hesitant fuzzy linguistic term set (IV2THFLTS) based on the interval number and the hesitant fuzzy linguistic term set. Then, we define some interval-valued 2-tuple hesitant fuzzy linguistic aggregation operators. Afterwards, to overcome the instability of subjective weights, we propose a method to compute the weights of attributes. For the convenience of application, a method is given to solve the multiple attribute decision making problems with IV2THFLTSs. Finally, a case study is carried out to validate the proposed method, and some comparisons with other methods are given to show the advantages of the proposed method.The work was supported in part by the National Natural Science Foundation of China (Nos. 71501135, 71771156), the China Postdoctoral Science Foundation (2016T90863, 2016M602698), the Fundamental Research Funds for the central Universities (No. YJ201535), and the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23).Si, G.; Liao, H.; Yu, D.; Llopis Albert, C. (2018). Interval-valued 2-tuple hesitant fuzzy linguistic term set and its application in multiple attribute decision making. Journal of Intelligent & Fuzzy Systems. 34(6):4225-4236. https://doi.org/10.3233/JIFS-171967S4225423634
Underground Mining Method Selection With the Hesitant Fuzzy Linguistic Gained and Lost Dominance Score Method
Underground mining method selection is a critical decision problem for available underground
ore deposits in exploitation design. As many comprehensive factors, such as physical parameters, economic
benefits, and environmental effects, are claimed to be established and a group of experts are involved in the
issue, the underground mining method selection is deemed as a multiple experts multiple criteria decision
making problem. Classical mining method assessment exists some gaps due to the way of representing
opinions. To address this matter, a hesitant fuzzy linguistic gained and lost dominance score method is
investigated in this paper. To enhance the flexibility and gain more information, mining planning engineers
are allowed to convey their knowledge using hesitant fuzzy linguistic term sets in the underground mining
method selection process. A novel score function of hesitant fuzzy linguistic term set is introduced to compare any hesitant fuzzy linguistic term sets. Then, based on the score function, a weight determining function
is proposed to calculate the weights of criteria, which can magnify the ‘‘importance’’ and ‘‘unimportance’’
of criteria. To select the mining method, the hesitant fuzzy linguistic gained and dominance score method
is developed. A case study concerning selecting a extraction method for a real mine in Yunnan province of
China is presented to illustrate the applicability of the proposed method. The effectiveness of the proposed
method is finally verified by comparing with other ranking methodsNational Natural Science Foundation of China under Grant 71501135 and Grant 717711562019 Sichuan Planning Project of Social Science under Grant SC18A0072018 Key Project of the Key Research
Institute of Humanities and Social Sciences in Sichuan Province under Grant Xq18A01 and Grant LYC18-02Electronic
Commerce and Modern Logistics Research Center Program, Key Research Base of Humanities and Social Science, Sichuan Provincial
Education Department, under Grant DSWL18-2Spark Project of Innovation, Sichuan University, under Grant 2018hhs-43Scientific Research Foundation for Excellent Young Scholars, Sichuan University, under Grant 2016SCU04A23
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
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
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