41 research outputs found
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence
Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.National Natural Science Foundation of China (NSFC) 71971039
71421001,71910107002,71771037,71874023
71871149Sichuan University sksyl201705
2018hhs-5
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
Pythagorean 2-tuple linguistic power aggregation operators in multiple attribute decision making
In this paper, we investigate the multiple attribute decision making
problems with Pythagorean 2-tuple linguistic information.
Then, we utilize power average and power geometric operations
to develop some Pythagorean 2-tuple linguistic power aggregation
operators: Pythagorean 2-tuple linguistic power weighted
average (P2TLPWA) operator, Pythagorean 2-tuple linguistic power
weighted geometric (P2TLPWG) operator, Pythagorean 2-tuple linguistic
power ordered weighted average (P2TLPOWA) operator,
Pythagorean 2-tuple linguistic power ordered weighted geometric
(P2TLPOWG) operator, Pythagorean 2-tuple linguistic power
hybrid average (P2TLPHA) operator and Pythagorean 2-tuple linguistic
power hybrid geometric (P2TLPHG) operator. The prominent
characteristic of these proposed operators are studied. Then,
we have utilized these operators to develop some approaches to
solve the Pythagorean 2-tuple linguistic multiple attribute decision
making problems. Finally, a practical example for enterprise
resource planning (ERP) system selection is given to verify the
developed approach and to demonstrate its practicality and
effectiveness
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
Expanding Grey Relational Analysis With the Comparable Degree for Dual Probabilistic Multiplicative Linguistic Term Sets and Its Application on the Cloud Enterprise
Under the cloud trend of enterprises, how do traditional businesses get on the cloud becomes a
worth pondering question. To help those traditional businesses that have no experience to dispel the clouds
and see the sun as soon as possible, we are planning to choose one corporation with rich experience to take
them into the cloud market. The quintessence of dual probabilistic linguistic term sets (DPLTSs) is that it uses
the combination of several linguistic terms and their proportions to reveal decision information by opposite
angles. This paper proposes the dual probabilistic multiplicative linguistic preference relations (DPMLPRs)
based upon the dual probabilistic multiplicative linguistic term sets (DPMLTSs). Then, it de nes the
comparable degree between the DPMLPRs and studies the consensus of the group DPMLPR. Moreover,
it probes the expanding grey relational analysis (EGRA) under the proposed comparable degree between the
DPMLTSs. After that, one example of choosing the experienced cloud cooperative partner is simulated under
the dual probabilistic linguistic circumstance. Besides, the comparative analysis is performed by considering
the similarity among the EGRA, TODIM, and VIKOR.Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX18_0199Scientific Research Foundation of the Graduate School of Southeast University under Grant
YBJJ1832FEDER Financial Support under Grant TIN2016-75850-
A hesitant fuzzy SMART method based on a new score function for information literacy assessment of teachers
As two powerful and flexible tools for decision-makers (DMs) to
model the complex cognition, the hesitant fuzzy set (HFS) and
hesitant fuzzy linguistic term set (HFLTS) allow DMs to express
their opinions with several possible membership values or linguistic
terms on the objects over each criterion. The aim of this article
is to develop a novel score function of the HFS and HFLTS including
hesitant degree and fuzzy degree information. For this purpose,
the notion of fuzzy degree of the hesitant fuzzy element
(HFE) and hesitant fuzzy linguistic element (HFLE) is introduced
first. Then, considering both the hesitant degree and fuzzy degree
information in expressions, the new score function, namely the
Score-H&FD, is designed. Based on which, we extend the classical
SMART (simple multi-attribute rating technique) method to the
hesitant fuzzy environment. As a result, the hesitant fuzzy SMART
(HF-SMART) method is developed in this article. Afterwards, we
apply our proposed approach to assess and rank several teachers
concerning information literacy. Finally, sensitive analysis and
comparative analysis are carried out. The results show that the
proposed method in this article has substantial advantages and
applicability
The generalized dice similarity measures for multiple attribute decision making with hesitant fuzzy linguistic information
In this paper, we shall present some novel Dice similarity measures of hesitant fuzzy linguistic term sets and the generalized Dice similarity measures of hesitant fuzzy linguistic term sets and indicate that the Dice similarity measures and asymmetric measures (projection measures) are the special cases of the generalized Dice similarity measures in some parameter values. Then, we propose the generalized Dice similarity measures-based multiple attribute decision making models with hesitant fuzzy linguistic term sets. Finally, a practical example concerning the evaluation of the quality of movies is given to illustrate the applicability and advantage of the proposed generalized Dice similarity measure