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This study aims to present a multiattribute decision-making (MADM) and clustering method to explore the ranking, classifications and evolution mechanisms of the research fronts in the Web of Science Essential Science Indicators (ESI) database. First, bibliometrics are used to reveal the characteristics of the 57 ESI research fronts with more than 40 ESI highly cited papers (ESI-HCPs) for each research front. Second, the eight representative indicators are discovered to get answers to the following two questions: (i) Who publishes the ESI-HCPs that form a research front? and (ii) Where citations to these ESI-HCPs come from on a research front? Next, we investigate the ranking and clusters among the 57 ESI research fronts using the MADM and k -means clustering method and uncover the evolution process of the research fronts in different clusters based on the representative indicators. We also compare the performances of different countries in these research fronts and find that the USA and China are the leading countries in most research fronts. However, the two countries behave differently with regard to the rankings, the classifications and the evolution. [ABSTRACT FROM AUTHOR]

In a very open competitive context where pure online players are consistently gaining market share, the use of digital devices is a steady trend which is penetrating physical retail stores as a tool for retailers to improve customer experience and increase engagement. This need has increased with the COVID-19 pandemic as electronic devices in physical stores reduce the contact between people providing a greater sense of health safety, hence improving the customer experience. This work develops a multiple-criteria decision-making model for retailers who want to digitize their physical stores, providing a systematic approach to manage investment priorities in the organization. Important decisions should involve all different areas of the organization: Finance, Clients, Internal Processes and Learning & Growth departments. This strategic decision can be made hierarchically to obtain consistent decisions, also the use of the Order Weighted Average operator allows for alternative scenarios to be presented and agreed among the different areas of the business. The authors develop a use case for a Spanish fashion retailer. In the most widely agreed scenario the preferred devices were more technologically complex and expensive, while in the scenarios where the head of Finance is more predominant, cheaper and simpler devices were selected. [ABSTRACT FROM AUTHOR]

This article provides a brief tour through the main fuzzy and linguistic decision-making trends, studies, methodologies, and models developed in the last 50 years. Fuzzy and linguistic decision-making approaches allow to address complex real-world decision problems where humans exhibit vagueness, imprecision, and/or use natural language to assess decision alternatives, criteria, etc. The aim of this article is threefold. First, the main fuzzy set theory and computing with words-based representation paradigms of decision information, with their different levels of expressive richness and complexity, are reviewed. Second, three core decision-making frameworks are examined: 1) multicriteria decision making; 2) group consensus-driven decision making; and 3) multiperson multicriteria decision making. Third, the article discusses new complex decision-making frameworks that have emerged in recent years, where decisions are guided by the “wisdom of the crowd”: their associated challenges are highlighted and considerations on much needed key guidelines for future research in the field are provided. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we initially re-define the existing definition of extended hesitant fuzzy set (EHFS) using the Cartesian product of hesitant fuzzy sets (HFSs). This is while, the EHFS concept should be defined in accordance with the number of decision makers', and not the number of HFSs. Then we show the superiority of the proposed EHFS to the existing HFS concept by taking multiple criteria group decision making (MCGDM) problems into account in which two nonlinear optimization models are used to evaluate the weights of the decision makers and the weights of the criteria, respectively. As will be discussed in more details, the first optimization model minimizes the divergence degree of individual form of extended hesitant fuzzy decision (IEHFD) matrices, is carried out in Step 2 of the proposed algorithm for solving the MCGDM problem and the second one minimizes the divergence degree between each IEHFD matrix and the collective form of extended hesitant fuzzy decision (CEHFD) matrix, is carried out in Step 3 of the proposed algorithm for solving the MCGDM problem. Finally, we will illustrate the applicability of the proposed method to the other existing ones by dealing with a MCGDM problem under both the hesitant and the extended hesitant fuzzy environments. In addition to that, we will show that the proposed procedure leads to the more desirable result of deciding in comparison with the other existing methods, especially when we are going to generate the criteria weights. Highlights • A new definition of concept of extended hesitant fuzzy set is presented. • The new proposal is based on Cartesian product of hesitant fuzzy set. • This novel model allows to handle better in decision making problems the weighted information of weights for both criteria and decision makers. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making (MCDM) techniques have attracted more and more scholars attention for their potential of application in many areas of human action. Although several contributions exist regarding comparative analysis of MCDM techniques, most of them are focused on demonstrating the similarities and differences of these methodologies in obtaining group decisions. However, the existing techniques comparing MCDM methods to investigate the most suitable ranking method for the case study have a critical shortcoming that limits their application to just MCDM methods resulting in total ranking order. This work contributes to reduce this shortcoming by establishing a correspondence between a non-total ranking order and a set of total ranking orders what we will call dominance-vector hesitant fuzzy set. [ABSTRACT FROM AUTHOR]

The Information and Communication Technologies (ICTs) play an important role in the economic development, making it necessary to assess the quality of service perceived by consumers in this sector. The most effective quality assessment from the consumer perspective is still to be researched, yet the most common approach is oriented towards quantitative indicators. This study proposes to use a two-dimensional model that combines the widely accepted segmentation of ICTs with elements from the SERVQUAL quality model. This model, useful in multi-criteria decision-making situations, has been developed using the 2-tuple linguistic representation and fuzzy logic principles. This methodology prevents data loss during processing and provides relevant information through 16 indicators related to the quality of service. Besides, an expert-based mechanism is defined for the use of historical information extracted from completed surveys. As a practical case, this mechanism is applied to the historical information of a telecommunications company for assessing the quality of the service provided to its customers. [ABSTRACT FROM AUTHOR]

The majority of businesses in the Information and Communications Technology (ICT) sector face decision-making problems on a daily basis. Most of these problems are based on contexts of uncertainty, where decisions are founded on qualitative information which may be imprecise or perception-based. In these cases, the information which is expressed by experts and users of evaluated services can be treated using processes of computing with words (CW). In this paper, we present a hybrid decision-making model especially designed for the ICT sector whereby the experts have the support of an intelligent system which provides information about the opinions of users related to those problems which are to be analysed. These opinions are obtained by using different mechanisms and techniques when users conduct business with the service provider. In addition, we employ a procedure for obtaining consensus between experts which enriches and strengthens the decision-making process. [ABSTRACT FROM PUBLISHER]

In this paper, we present a consensus model for multiperson decision making (MPDM) problems with different preference structures based on two consensus criteria: 1) a consensus measure which indicates the agreement between experts' opinions and 2) a measure of proximity to find out how far the individual opinions are from the group opinion. These measures are calculated by comparing the positions of the alternatives between the individual solutions and collective solution. In such a way, the consensus situation is evaluated in each moment in a more realistic way. With these measures, we design a consensus support system that is able to substitute the actions of the moderator. In this system, the consensus measure is used to guide the consensus process until the final solution is achieved while the proximity measure is used to guide the discussion phases of the consensus process. The consensus support system has a feedback mechanism to guide the discussion phases based on the proximity measure. This feedback mechanism is based on simple and easy rules to help experts change their opinions in order to obtain a degree of consensus as high as possible. The main improvement of this consensus model is that it supports consensus process automatically, without moderator, and, in such a way, the possible subjectivity that the moderator can introduce in the consensus process is avoided. [ABSTRACT FROM AUTHOR]

As a consequence of the exponential growth in online data, tourism sector has experimented a radical transformation. From this large amount of information, opinion makers can be benefited for decision making in their purchase process. However, it can also harm them according to the information they consult. In fact, being benefited or harmed by the information translates into greater or lesser satisfaction after the purchase. This will largely depend on the published opinions that they take into account, which in turn depend on the value of the opinioner who publishes said information. In this paper, the authors propose a methodology that integrates multiple decision-making techniques and with which it is intended to obtain a ranking of hotels through the opinions of their past clients. To do this, the customer value is obtained using the Recency, Frequency, Helpfulness model. The information about the users found in the social networks is managed and aggregated using the fuzzy linguistic approach 2-tuples multi-granular. In addition, we have verified the functionality of this methodology by presenting a business case by applying it on TripAdvisor data. • A new linguistic MCDM methodology based on AHP and RFH 2-tuple model is presented. • This MCDM methodology is used to define a hotels evaluation model. • The hotels evaluation model is based on objective and subjective information. • The hotels evaluation model increases the hotels competitiveness. [ABSTRACT FROM AUTHOR]

The use of dual probabilistic linguistic term sets (DPLTSs) to represent the use's preferences in decision making can reflect the decision maker's cognitive certainty and uncertainty. Additionally, the appearance of incomplete preferences is a recurring phenomenon that must be taken into account if you want to make a successful decision. This paper presents a new multi-criteria decision model based on the incomplete dual probabilistic linguistic preference relations (IDPLPRs). We first propose a step-by-step repairing method to repair the linguistic section and probabilistic section of IDPLPRs separately. The superiority is that this step-by-step method conforms to the principle of element generation. After that, the consistency index based on the distance measure between the dual probabilistic linguistic preference relations (DPLPRs) is defined to check and improve the consistency of DPLPRs. Then the weights of criteria can be obtained by information fusion. Moreover, we construct optimistic and pessimistic data envelopment analysis models under the dual probabilistic linguistic environment to do the sorting process. Optimistic and pessimistic data envelopment analysis models can demonstrate the efficiency of each decision-making unit (DMU) from the perspective of the most and least favorable. Finally, we simulate a cased of 5G industry market to help enterprises choose appropriate 5G partners by using proposed methods. • Define the IDPLPRs. • Repair the IDPLPRs. • Check and improve the consistency of DPLPRs. • Propose the optimistic and pessimistic dual probabilistic DEA model. [ABSTRACT FROM AUTHOR]

Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via the procedure named criteria weighting through the attention of the experts. We evaluate the methodology in a case study of restaurant choice using TripAdvisor reviews, hence we build, manually annotate, and release the TripR-2020 dataset of restaurant reviews. We analyze the SA-MpMcDM methodology in different scenarios using and not using natural language and numerical evaluations. The analysis shows that the combination of both sources of information results in a higher quality preference vector. • Decision making models are limited by pre-defined numerical and linguistic terms. • We propose a methodology to deal with natural language and numerical assessments. • We design a deep learning model for distilling opinions from written assessments. • We present and release a dataset, which can be used for evaluating MpMcDM models. [ABSTRACT FROM AUTHOR]

In this paper, a novel multi-criteria three-way decision (TWD) method is devised to solve the problems of classification and sorting by considering requirements and targets based on qualitative and quantitative information. TWD theory has been generally utilized to handle multi-criteria decision-making problems. However, few studies on the TWD theory consider the requirements and targets of decision-makers. To fill this gap in the existing studies on the TWD theory, the conditional probability is computed based on the fuzzy axiomatic design and the target-based aggregation operator. A parameter is designed to reflect the preference degree of the basic requirements over the target values offered by the decision-maker. To meet the description needs of quantitative and qualitative attributes, the real numbers and the interval numbers are employed to describe the quantitative information, the interval-valued double hierarchy linguistic term sets are employed to express the qualitative information. The analytic hierarchy process method is utilized to calculate the weights of the criteria with interval-valued double hierarchy linguistic information. Following the maximum-utility principle, the classification and ranking of the objects are attained. Ultimately, a case study on the selection of algorithm engineers in the recruitment process verifies the practicability and effectiveness of the proposed method, and a comparative analysis with other methods proves the superiority of this method. • Basic requirements and target values are considered in the three-way decision. • Quantitative and qualitative expressions are used to describe the criterion values. • Our proposed method can not only classify the objects but also rank them. [ABSTRACT FROM AUTHOR]

In general, traditional decision-making models are based on methods that perform calculations on quantitative measures. These methods are usually applied to assess possible solutions to a problem, resulting in a ranking of alternatives. However, when it comes to making decisions about qualitative measures —such as service quality—, the quantitative assessment is a bit difficult to interpret. Therefore, taking into account the maturity of the linguistic assessment models, this paper puts forth a new solution proposal. It is a decision-making model that uses linguistic labels —represented with the 2-tuple notation— and a variable expressive richness when providing output results. This solution allows expressing results in a manner closer to the human cognitive system. To achieve this goal, a mechanism has been implemented for measuring the distance among the aggregate ratings, providing the decision-maker with a fast and intuitive answer. The proposal is illustrated with an application example based on the TOPSIS model, using linguistic labels throughout the entire process. [ABSTRACT FROM AUTHOR]

Tender analysis processes are everyday processes in any company, but they become even more important when the financial resources are limited. Achieving optimal and transparent tender analysis processes requires hard work from everyone involved. This paper proposes a linguistic multi-criteria decision-making model which helps decision-makers in this task by automating it. To do that, the proposal requires diverse experts to assess different criteria and establish their corresponding weights. The processing of all these input data will determine a minimum value, called profile , to consider an alternative as valid. Additionally, assessments of a similar (probably external) process will be taken into account, becoming a reference valuation. Unlike other decision-making models, the use of this last value gives an idea of the goodness of the result depending on whether the solution obtained is close to the reference value. To the best of our knowledge, there is no expert or intelligent system specifically designed to fully meet the needs of tender analysis processes. For both input and output process stages carried out in our proposal, 2-tuple linguistic labels have been used. These linguistic labels were chosen to facilitate decision-making for the staff involved in the process, as well as for being the most suitable communication way used by human beings. To validate the model, we apply it to a case study in the ICT (Information and Communications Technology) sector. In addition, we include a literature review related to applications using the 2-tuple representation, as well as a comparison of our proposal with related methods, including the results obtained by these methods for the case study presented. [ABSTRACT FROM AUTHOR]