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

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

Fusing hotel ratings and reviews with hesitant terms and consensus measures

People have come to refer to reviews for valuable information on products before making a purchase. Digesting relevant opinions regarding a product by reading all the reviews is challenging. An automated methodology which aggregates opinions across all the reviews for a single product to help differentiate any two products having the same overall rating is defined. In order to facilitate this process, rating values, which capture the overall satisfaction, and written reviews, which contain the sentiment of the experience with a product, are fused together. In this manner, each reviewer’s opinion is expressed as an interval rating by means of hesitant fuzzy linguistic term sets. These new expressions of opinion are then aggregated and expressed in terms of a central opinion and degree of consensus representing the agreement among the sentiment of the reviewers for an individual product. A real case example based on 2506 TripAdvisor reviews of hotels in Rome during 2017 is provided. The efficiency of the proposed methodology when discriminating between two hotels is compared with the TripAdvisor rating and median of reviews. The proposed methodology obtains significant differentiation between product rankings.This research has been partially supported by the Secretary of Universities and Research of the Department of Enterprise and Knowledge of the Generalitat de Catalunya (2017 DI 086) and by the INVITE Research Project (TIN2016-80049-C2-1-R and TIN2016-80049-C2-2-R (AEI/FEDER, UE)), funded by the Spanish Ministry of Science and Information Technology.Peer ReviewedPostprint (author's final draft

An Overview on Fuzzy Modelling of Complex Linguistic Preferences in Decision Making

This work is partially supported by the Spanish National research project TIN2015-66524-P, Spanish Ministry of Economy and Finance Postdoctoral Training (FPDI-2013-18193) and ERDF.Decision makers involved in complex decision making problems usually provide information about their preferences by eliciting their knowledge with different assessments. Usually, the complexity of these decision problems implies uncertainty that in many occasions has been successfully modelled by means of linguistic information, mainly based on fuzzy based linguistic approaches. However, classically these approaches just allow the elicitation of simple assessments composed by either one label or a modifier with a label. Nevertheless, the necessity of more complex linguistic expressions for eliciting decision makers’ knowledge has led to some extensions of classical approaches that allow the construction of expressions and elicitation of preferences in a closer way to human beings cognitive process. This paper provides an overview of the broadest fuzzy linguistic approaches for modelling complex linguistic preferences together some challenges that future proposals should achieve to improve complex linguistic modelling in decision making.Spanish National research project TIN2015-66524-PSpanish Ministry of Economy and Finance Postdoctoral Training FPDI-2013-18193European Union (EU

Cargo company recommendation study based on probabilistic linguistic term set

The global economic structure is the main reason for changes in consumption habits and consumer behavior. Developing information technologies direct producers and consumers to e-commerce. Cargo services are an important link in the chain in the fast and effective operation of e-commerce. The growth in e-commerce has a driving force in the development of cargo services and cargo companies. Cargo companies can survive in global competition by being preferred by customers and increasing their number of customers. The change in the number of customers occurs by communicating the satisfaction or dissatisfaction with the cargo company to potential customers. This study deals with the preference levels of cargo companies serving in Turkey according to customer suggestions. The data obtained from the survey evaluations are processed and recommendation ranking calculations are made for cargo companies. Probabilistic Linguistic Term Sets (PLTS) are used to eliminate customer ambiguities in survey evaluations. Alternative cargo company recommendations are ranked based on the customers' past service experiences from cargo companies. Aras Cargo, MNG Cargo, PTT Cargo, Surat Cargo, UPS Cargo, Yurtiçi Cargo companies are evaluated according to price, personnel, speed, reliability and network attributes. The maximum deviation optimization method based on the Lagrangian function is used to calculate the weights of the cargo companies' attributes. The probabilistic linguistic cosine similarity method compares cargo companies pairwise under attributes and a similarity matrix is obtained for six cargo companies. The similarity matrix defines the alternative cargo company recommendation ranking based on customers' past experiences. UPS, SURAT and MNG cargo companies stand out as the most prioritized companies according to the evaluation results. The effects of attribute weights are observed by designing six different scenarios and it is observed that the differentiating attribute weights affect the recommendation ranking. Spearman correlation coefficient evaluation based on recommendation rankings indicates a high relationship between attributes.Publisher's Versio

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