Interval Consistency Repairing Method for Double Hierarchy Hesitant Fuzzy Linguistic Preference Relation and Application in the Diagnosis of Lung Cancer

Natural language is more in line with the real thoughts of people than crisp numbers considering that qualitative language information is more consistent with the expression habits of experts. Double hierarchy hesitant fuzzy linguistic preference relation (DHHFLPR) can be used to express complex linguistic preference information accurately because the pairwise comparison methods are more accurate than non-pairwise methods. Consistency reflects the rationalization of a preference relation and can be used to judge whether a preference relation is self-contradictory or not. In this paper, an interval consistency index of DHHFLPR is developed, which is consisted by the consistency indices of all double hierarchy linguistic preference relations associated with the DHHFLPR. Additionally, an average consistency index of DHHFLPR is given by calculating the average value of the consistency indices of all double hierarchy linguistic preference relations. Moreover, we develop a consistency checking and repairing method for DHHFLPR. Finally, we apply the proposed method into a practical group decision-making problem that is to identify the most critical factors in developing lung cancer, and some comparative analyses involving the connections and differences among the proposed consistency indices are analysed

A survey on energy justice: a critical review of the literature

The increasing scarcity of resources and the escalating complexity of reality make the fairness ensuring in energy activities even more difficult. In this context, energy justice, as an emerging cross-field, tries to provide solutions based on practical problems. In the face of the surge of energy justice publications, it is necessary to review them in time, so that we can comprehend the significant achievements and the research directions worthy of further exploration. With the help of visualization tools, this paper conducts a comprehensive quantitative analysis of 1,910 energy justice publications. Based on the results, we reach the following main conclusions: (1) The energy justice publications have only emerged rapidly in recent years; (2) The research hotspots are closely related to the renewable energy transition; (3) The distribution of prominent contributors in this field is relatively concentrated. The main contribution of this study is to comprehensively display the essential characteristics of the literature in this field, such as the evolutions of research themes and the performances of research contributors in different dimensions, so as to provide readers with an effective way to understand the knowledge structure in this field, and help related researchers rationally examine the existing results

Probabilistic double hierarchy linguistic alternative queuing method for real economy development evaluation under the perspective of economic financialization

With the development of science and technology, the new road of scientific economic and financial development has played a decisive role in supporting the financial undertaking. To accelerate the economic development, it is very important to increase the guiding role of financial undertaking in the real economy. Therefore, it is necessary to promote the development of the real economy under the perspective of economic financialization based on some actions. To judge the implementation effect of these actions, this paper develops a multiple criteria decisionmaking (MCDM) method to evaluate them. First, the decisionmaking matrices are established with the probabilistic double hierarchy linguistic term set in which the probabilities are added to all double hierarchy linguistic terms. Additionally, a weightdetermining method is developed to obtain the weight vector of criteria, and we develop a MCDM method named the probabilistic double hierarchy linguistic alternative queuing method (PDHLAQM), where the decision-making result is intuitive by a directed graph or a 0–1 precedence relationship matrix. Furthermore, we apply the PDHL-AQM to solve a practical MCDM problem involving the real economy development evaluation under the perspective of economic financialization. Finally, some comparative analyses are made to show the advantages and reasonableness of the PDHL-AQM

The risk assessment of construction project investment based on prospect theory with linguistic preference orderings

Multiple experts decision-making (MEDM) can be regarded as a situation where a group of experts are invited to provide their opinions by evaluating the given alternatives, and then select the optimal alternative(s). As a useful linguistic expression model, linguistic preference orderings (LPOs) were established in which the order of alternatives and the relationships between two adjacent alternatives are fused well. Considering that prospect theory has the superiority in depicting risk attitudes (risk seeking for losses and risk aversion for gains) during the uncertain decision-making process, this paper develops a consensus model based on prospect theory to deal with MEDM problems with LPOs. Firstly, each LPO provided by expert is transformed into the responding DHLPR with complete consistency. Then, the reference point of expert is determined and the prospect preference matrix is established. Moreover, we can obtain the overall prospect consensus degree for a MEDM problem by calculating the similarity degree between individual and collective prospect preference matrix. Furthermore, a consensus improvement method is developed to complete the consensus reaching process. Finally, we apply the proposed method to deal with a practical MEDM problem involving the construction project investment, and make some comparative analyses with existing methods.National Natural Science Foundation of China (NSFC) 71771155China Postdoctoral Science Foundation 2020M680151Sichuan Postdoctoral Science special FoundationSichuan University Postdoctoral Interdisciplinary Innovation Startup FoundationFundamental Research Funds for the Central Universities YJ202015European Union (EU) TIN2016-75850-RSichuan Province System Science and Enterprise Development Research Center Xq20B0

Managing consensus by multi-stage optimization models with linguistic preference orderings and double hierarchy linguistic preferences

Preference ordering structures are useful and popular tools to represent experts’ preferences in the decision making process. In the existing preference orderings, they lack the research on the precise relationship between any two adjacent alternatives in the preference orderings, and the decision making methods are unreasonable. To overcome these issues, this paper establishes a novel concept of linguistic preference ordering (LPO) in which the ordering of alternatives and the relationships between two adjacent alternatives should be fused well, and develops two transformation models to transform each LPO into the corresponding double hierarchy linguistic preference relation with complete consistency. Additionally, to fully respect the experts’ expression habits and provide more refined solutions to experts, this paper establishes a multi-stage consensus optimization model by considering the suggested preferences represented in both the continuous scale and the discrete scale, and develops a multi-stage interactive consensus reaching algorithm to deal with multi-expert decision making problem with LPOs. Furthermore, some numerical examples are presented to illustrate the developed methods and models. Finally, some comparative analyses between the proposed methods and models and some existing methods have been made to show the advantages of the proposed methods and models. First published online 24 February 202

Bibliometric overview and retrospective analysis of fund performance research between 1966 and 2019

Fund performance has been a hot topic in the financial research area, fair and correct evaluation of fund performance is of great significance for fund investors and companies. However, most of the relevant publications do not have any retrospective analysis of this topic in terms of knowledge domain to show its development trends and research concerns. To address this issue, two effective bibliometric tools namely Citespace II (The 5.3.R4 Edition) and SciMat are used to analyze the knowledge domain of this field in this paper. We have analyzed 979 articles related to fund performance from Web of Science between 1966 and 2019 (July), the analysis content includes the current status, collaboration network, co-citation network, and emerging trends of fund performance research, then we have derived the following desired conclusions: (1) In the last twenty years, there was a significant increase in the publication and citation numbers of fund performance research; especially, the relative research has become interdisciplinary and internationalized. (2) “Mutual Fund Performance”, “Fund Return”, “Investment Performance”, and “Portfolio Selection” are the hottest topics in the fund performance research. (3) “Small Fund” and “Investor Reaction” are the two emerging trends in the fund performance research. To sum up, there are two main contributions in this paper: First, we provide a full bibliometric analysis about the fund performance research. Second, we make the further development of fund performance research easier and more clearly to show the directions to learn and study for beginners

Automobile components procurement using a DEA-TOPSIS-FMIP approach with all-unit quantity discount and fuzzy factors

Components procurement is a crucial process in supply chain management of the automobile industry. The problem is further complicated by imprecise information and discount policies provided by suppliers. This paper aims to develop a computational approach for assisting automobile components procurement with all-unit quantity discount policy and fuzzy factors, from potential suppliers offering different product portfolios. We propose a two-stage approach consisting of a DEA-TOPSIS (data envelopment analysis procedures followed with a technique for order preference by similarity to an ideal solution) approach for screening suppliers, and subsequentially a fuzzy mixed integer programming (FMIP) model with multiple objectives for optimizing order allocations. The DEA-TOPSIS approach integrates suppliers’ comparative performance and diversity performance into an overall index that improves the ranking of potential suppliers, while the FMIP model features a soft time-window in delivery punctuality and an all-unit quantity discount function in cost. By applying it in a case of automobile components procurement, we show that this two-stage approach effectively supports decision makers in yielding procurement plans for various components offered by many potential suppliers. This paper contributes to integrating multi-attribute decision analysis approach in the form of DEA crossevaluation with TOPSIS and FMIP model for supporting components procurement decisions. First published online 19 November 202

Double hierarchy linguistic preference information: consistency, consensus and large-scale group decision making

In this thesis, we focus on the discussions about three main aspects. Firstly, we mainly analyze the basic concepts of DHLTS and DHHFLTS, propose some equivalent transformation functions, and then develop some operations and properties of DHHFLTSs. In addition, considering that the distance and similarity measures are fundamentally important in amounts of research fields, we define the axioms of distance and similarity measures between two DHHFLTSs, and then introduce a series of distance and similarity measures between two DHHFLTSs. Secondly, considering that more and more experts prefer to give their preferences by making pairwise comparisons between any two alternatives, meanwhile this kind of preference reflects the relationships between different alternatives intuitively. Therefore, preference relation becomes one of the popular and effective tools. Based on the DHHFLTS and preference form, we give a concept of double hierarchy hesitant fuzzy linguistic preference relation (DHHFLPR). Then, to avoid the occurrence of some self-contradictory situations, it is very important to carry out the consistency checking and improving process for each DHHFLPR in GDM process. Therefore, we discuss some additive consistency measures for DHHFLPRs. For the purpose of judging whether a DHHFLPR is of acceptable consistency or not, we define a consistency index of DHHFLPR and develop a novel method to improve the existing methods for calculating the consistency thresholds. Then we present two convergent consistency repairing algorithms based on automatic improving method and feedback improving method respectively to improve the consistency index of a given DHHFLPR with unacceptable consistency. Finally, with the progress of science and technology and the development of network environment, the communications between people are increasingly convenient. Large-scale group decision making (LSGDM) has become the focuses of decision-making problems. Generally, a GDM problem can be called LSGDM problem when the number of experts is more than 20 [LC06]. This thesis mainly studies LSGDM from two aspects. 1) We discuss the clustering method and the consensus reaching process in LSGDM with double hierarchy hesitant fuzzy linguistic preference information. We also propose the similarity degree-based clustering method, the double hierarchy information entropy-based weights-determining method and the consensus measures. 2) In LSGDM, sometimes some experts do not modify their preferences or even do it on the contrary way to the remaining experts, and some different opinions or minority preferences are often cited as obstacles to decision making [PMH14, XDC15]. Therefore, this thesis gives a concept of double hierarchy linguistic preference relation (DHLPR) and develops a consensus model to manage minority opinions and non-cooperative behaviors in LSGDM with DHLPRs. Additionally, to establish the consensus model, some basic tools such as the distance-based cluster method, the weight-determining method, and the comprehensive adjustment coefficient-determining method are developed. In addition to the discussions of the core knowledge of DHLTS, DHHFLTS, DHLPR and DHHFLPR, this thesis discusses some different decision making models under different decision making contexts. We mainly discuss three different decision making contexts, i.e., multiple criteria decision making (MCDM), GDM, and LSCDM.Tesis Univ. Granada.FInancial support from China Scholarship Counci

Double hierarchy linguistic preference information: consistency, consensus and large-scale group decision making

In this thesis, we focus on the discussions about three main aspects. Firstly, we mainly analyze the basic concepts of DHLTS and DHHFLTS, propose some equivalent transformation functions, and then develop some operations and properties of DHHFLTSs. In addition, considering that the distance and similarity measures are fundamentally important in amounts of research fields, we define the axioms of distance and similarity measures between two DHHFLTSs, and then introduce a series of distance and similarity measures between two DHHFLTSs. Secondly, considering that more and more experts prefer to give their preferences by making pairwise comparisons between any two alternatives, meanwhile this kind of preference reflects the relationships between different alternatives intuitively. Therefore, preference relation becomes one of the popular and effective tools. Based on the DHHFLTS and preference form, we give a concept of double hierarchy hesitant fuzzy linguistic preference relation (DHHFLPR). Then, to avoid the occurrence of some self-contradictory situations, it is very important to carry out the consistency checking and improving process for each DHHFLPR in GDM process. Therefore, we discuss some additive consistency measures for DHHFLPRs. For the purpose of judging whether a DHHFLPR is of acceptable consistency or not, we define a consistency index of DHHFLPR and develop a novel method to improve the existing methods for calculating the consistency thresholds. Then we present two convergent consistency repairing algorithms based on automatic improving method and feedback improving method respectively to improve the consistency index of a given DHHFLPR with unacceptable consistency. Finally, with the progress of science and technology and the development of network environment, the communications between people are increasingly convenient. Large-scale group decision making (LSGDM) has become the focuses of decision-making problems. Generally, a GDM problem can be called LSGDM problem when the number of experts is more than 20 [LC06]. This thesis mainly studies LSGDM from two aspects. 1) We discuss the clustering method and the consensus reaching process in LSGDM with double hierarchy hesitant fuzzy linguistic preference information. We also propose the similarity degree-based clustering method, the double hierarchy information entropy-based weights-determining method and the consensus measures. 2) In LSGDM, sometimes some experts do not modify their preferences or even do it on the contrary way to the remaining experts, and some different opinions or minority preferences are often cited as obstacles to decision making [PMH14, XDC15]. Therefore, this thesis gives a concept of double hierarchy linguistic preference relation (DHLPR) and develops a consensus model to manage minority opinions and non-cooperative behaviors in LSGDM with DHLPRs. Additionally, to establish the consensus model, some basic tools such as the distance-based cluster method, the weight-determining method, and the comprehensive adjustment coefficient-determining method are developed. In addition to the discussions of the core knowledge of DHLTS, DHHFLTS, DHLPR and DHHFLPR, this thesis discusses some different decision making models under different decision making contexts. We mainly discuss three different decision making contexts, i.e., multiple criteria decision making (MCDM), GDM, and LSCDM.Tesis Univ. Granada.FInancial support from China Scholarship Counci

An overview of Big Data in Healthcare: multiple angle analyses

Big data have been in use since the 1990s, which usually include some complex data sets whose sizes are beyond the ability of commonly used software to handle within a reasonable period of time. In recent years, big data analytics by providing personalized medicine and regulation analysis, providing clinical risk intervention and forecast analysis, reducing waste and nursing patients with external and internal variability, standardization of medical terminology and patient registration, and fragmentation of the solution, help to improve health care. This paper provides an overview of the contents of big data healthcare. We summarize some kinds of medical big data, including the electronic health records, the medical image data, the healthcare system big data, the health Internet of Things and healthcare informatics, the remote medical monitoring big data, the biomedical big data, and other sources of big data. Furthermore, we discuss some methods for handling different kinds of medical big data. Additionally, we analyze the privacy of medical big data and summarize some methods and technologies to protect privacy. Aiming at some special cases, we list some other analyses and methods for them. Most importantly, we discuss the potential challenges and future research directions related to big data healthcare