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

Comprehensive Power Transformers Evaluation based on Multi-Criteria Decision-Making Approach

Selection of adequate power transformers is among the most important factors for ensuring well-operated and optimized power generation and distribution systems, which imposes the need for applying optimization methods in order to obtain comprehensive evaluation process. In the paper, a multi-criteria group decision-making supportive model for the power transformer evaluation is proposed; the model is based on integration of the Analytic Hierarchy Process (AHP) and the compromise ranking method with fuzzy Set Theory. The AHP method was utilized in order to estimate the criteria relative weights, whereas the compromise ranking method was used for alternative assessment and compromise ranking. Integration of the fuzzy logic within the proposed model allows dealing with problem of the ambiguities of human perceptions and provides more rational results. A numerical example illustrates the proposed methodology on the real MCDM problem of power transformers selection

Fuzzy multicriteria analysis and its applications for decision making under uncertainty

Multicriteria decision making refers to selecting or ranking alternatives from available alternatives with respect to multiple, usually conflicting criteria involving either a single decision maker or multiple decision makers. It often takes place in an environment where the information available is uncertain, subjective and imprecise. To adequately solve this decision problem, the application of fuzzy sets theory for adequately modelling the uncertainty and imprecision in multicriteria decision making has proven to be effective. Much research has been done on the development of various fuzzy multicriteria analysis approaches for effectively solving the multicriteria decision making problem, and numerous applications have been reported in the literature. In general, existing approaches can be categorized into (a) multicriteria decision making with a single decision maker and (b) multicriteria group decision making. Existing approaches, however, are not totally satisfactory due to various shortcomings that they suffer from including (a) the inability to adequately model the uncertainty and imprecision of human decision making, (b) the failure to effectively handle the requirements of decision maker(s), (c) the tedious mathematical computation required, and (d) cognitively very demanding on the decision maker(s). This research has developed four novel approaches for effectively solving the multicriteria decision making problem under uncertainty. To effectively reduce the cognitive demand on the decision maker, a pairwise comparison based approach is developed in Chapter 4 for solving the multicriteria problem under uncertainty. To adequately meet the interest of various stakeholders in the multicriteria decision making process, a decision support system (DSS) based approach is introduced in Chapter 5. In Chapter 6, a consensus oriented approach is presented in multicriteria group decision making on which a DSS is proposed for facilitating consensus building in solving the multicriteria group decision making problem. In Chapter 7, a risk-oriented approach is developed for adequately modelling the inherent risk in multicriteria group decision making with the use of the concept of ideal solutions so that the complex and unreliable process of comparing fuzzy utilities usually required in fuzzy multicriteria analysis is avoided. Empirical studies of four real fuzzy multicriteria decision making problems are presented for illustrating the applicability of the approaches developed in solving the multicriteria decision making problem. A hospital location selection problem is discussed in Chapter 8. An international distribution centre location problem is illustrated in Chapter 9. A supplier selection problem is presented in Chapter 10. A hotel location problem is discussed in Chapter 11. These studies have shown the distinct advantages of the approaches developed respectively in this research from different perspectives in solving the multicriteria decision making problem

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

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

Hotel selection with safe tourism certificates in Covid-19 pandemic using SWARA and fuzzy COPRAS methods

The virus, which emerged in Wuhan, China, has taken the whole world under its influence in a short time. The World Health Organization declared this period as the "pandemic period", which means a global epidemic, due to the spread of the virus to the world in a short time. This period has undoubtedly been a period when many habits changed for everyone. Vacation habit is also one of the habits that change during this period. Especially in the summer months, people who spend their holidays in hotels had to be more sensitive in choosing the hotel due to the continuation of the pandemic period. While different criteria were considered in hotel selection before, the criterion of having a safe tourism certificate has become the most important criterion due to the pandemic. Businesses with this certificate can provide service without any problems as they take all precautions during the pandemic period. In this study, a hotel selection problem that takes into account the criteria of safe tourism certificate is discussed. In this study, a hotel selection problem that takes into account the safe tourism certification criteria is discussed. In the study, Stepwise Weight Assessment Ratio Analysis (SWARA) and fuzzy logic based The Complex Proportional Assessment (COPRAS) methods were used to solve the problem of choosing the most suitable hotel. As a result of the study, ten hotel alternatives were evaluated according to the criteria of safe tourism certificate during the pandemic period and the most suitable hotel was selected

Understanding location decisions of energy multinational enterprises within the European smart cities’ context: An integrated AHP and extended fuzzy linguistic TOPSIS method

Becoming a smart city is one of the top priorities in the urban agenda of many European cities. Among the various strategies in the transition path, local governments seek to bring innovation to their cities by encouraging multinational enterprises to deploy their green energy services and products in their municipalities. Knowing how to attract these enterprises implies that political leaders understand the multi-criteria decision problem that the energy sector enterprises face when deciding whether to expand to one city or another. To this end, the purpose of this study is to design a new manageable and controllable framework oriented to European cities’ public managers, based on the assessment of criteria and sub-criteria governing the strategic location decision made by these enterprises. A decision support framework is developed based on the AHP technique combined with an extended version of the hesitant fuzzy linguistic TOPSIS method. The main results indicate the higher relative importance of government policies, such as degree of transparency or bureaucracy level, as compared to market conditions or economic aspects of the city’s host country. These results can be great assets to current European leaders, they show the feasibility of the method and open up the possibility to replicate the proposed framework to other sectors or geographical areas.The authors acknowledge the support from the European Union “Horizon 2020 Research and Innovation Programme” under the grant agreements No 731297. Also, this research has been partially supported 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 (published version

Hotel selection utilizing online reviews: a novel decision support model based on sentiment analysis and DL-VIKOR method

With the considerable development of tourism market, as well as the expansion of the e-commerce platform scale, increasing tourists often prefer to select tourism products such as services or hotels online. Thus, it needs to provide an efficient decision support model for tourists to select tourism products. Online reviews based on the user experience would help tourists improve decision efficiency on tourism products. Therefore, in this study, a quantitative method for hotel selection with online reviews is proposed. First, with respect this problem with online reviews, by analyzing sentiment words in online reviews, tourists’ sentiment preferences are transformed into the format of distribution linguistic with respect to sentiment levels. Second, from a theoretical perspective, we proposed a method to determine the ideal solution and nadir solution for distribution linguistic evaluations. Next, based on the frequency of words for evaluating hotel and the distribution linguistic evaluations, the weight vector of the evaluation features is determined. Further, a novel DL-VIKOR method is developed to rank and then to select hotels. Finally, a realistic case from TripAdvisor.com for selecting hotel is used to demonstrate practically and feasibility of the proposed model. First published online 19 July 201