Fuzzy Preference Relations Research Articles

Evaluating the Influence of Criteria Revitalization Strategy Implementation for the Hospitality Industry in the Post-Pandemic Era

This study applies consistent fuzzy preference relations (CFPR) to evaluate the influential criteria of revitalization strategies (RS) for the hospitality industry in the post-pandemic (COVID-19) era in Taiwan. A real case applies CFPR in order to analyze the relationship between governmental implementation and industrial expectations in Taiwan. The results indicate that “market revitalization”, such as the Taiwanese government’s implementation of various stimulus vouchers and coupons to encourage market consumption and revitalize the overall economy, is considered the most essential/important criteria for RS. This study strengthens the government sector by evaluating the heterogeneity of revitalization strategies best used to formulate the actions to pilot industries as a global contribution to fight the COVID-19 pandemic within a global crisis.

A genetic algorithm based on dual hesitant fuzzy preference relations for consensus group decision making

Consensus is an important issue in group decision making as it aims to avoid future contestation by the decision makers. The elicitation of criteria and decision makers’ weights is an essential part of decision-making problems. This study proposes a new consensus model for group decision making based on dual hesitant fuzzy and evolutionary algorithm for the definition of unknown criteria and decision makers’ weights. The Dual Hesitant Fuzzy Preference Relations combines the advantages of intuitionistic and hesitant fuzzy representations, and it is used to deal with the imprecision in decision makers’ judgments. In order to find decision makers weights to reach a better level of consensus without the need to modify initial assessments, the proposed Genetic Algorithm (GA) is applied. An illustrative application case is presented in a large steel company considering sustainable criteria. An instance generator was proposed to create different scenarios of decision making varying the number of criteria, decision makers, and level of hesitation. Several instances were used in the computational tests for the evaluation of the GA performance. The effectiveness of the proposed GA was verified by comparing its results with the results of the implemented Particle Swarm Optimization (PSO) algorithm. The GA yielded solutions with improved consensus levels in a reasonable runtime, especially for a small or medium number of decision makers.

Novel consistency and consensus of generalized intuitionistic fuzzy preference relations with application in group decision making

Novel consistency and consensus of generalized intuitionistic fuzzy preference relations with application in group decision making

Analysis of the critical success factors of mobile animation games based on a consistent fuzzy preference relationship

PurposeThe purpose of this research is to explore the critical success factors of mobile animation games, by exploring the game itself, information systems, game motivation and promotional activities, as well as conducting research and analysis on mobile animation game players.Design/methodology/approachThis research used the Analysis Hierarchy Process (AHP) method and the consistent fuzzy preference relationship for data analysis. In this study, collect 1,286 valid questionnaires through online questionnaire surveys. And comparing the two games “Legend Showdown” and “Tower of Gods and Demons”, players believe that the more successful mobile animation game is “Legend Showdown”.FindingsThrough experimental design, and the consistent fuzzy preference relationship for data analysis. The results found that the critical factors considered by the player in relation to the mobile animation game were firstly the information system, followed by promotional activities, game motivation and finally the game itself.Research limitations/implications Because this research does not involve the concept of fuzzy theory at all, it is easy to produce subjective, uncertain and ambiguity issues when comparing pairwise. We recommended that follow-up researchers can use fuzzy semantic preference relations to solve this problem.Originality/valueThis study proposed a new approach that takes the critical factors for the mobile animation game. According to the research results, the critical success factor of mobile animation games is the information system, as it could provide a reference direction for game manufacturers when designing or formulating marketing strategies in the future.

Water–Energy–Food nexus evaluation using an inverse approach of the graph model for conflict resolution based on incomplete fuzzy preferences

With the continuous development of economic globalization and the increasing strengthening of human exchanges, the Water–Energy–Food (WEF) nexus evaluation has become a new research area. The purpose of this paper is to develop an inverse approach of graph model for conflict resolution (GMCR) to the conflict problems of WEF nexus evaluation in real life. Specifically, due to lack of information, some decision makers’ (DMs’) preferences over states may be incomplete fuzzy preference relations. Therefore, an algorithm is devised to amend the incomplete fuzzy preference relation to the complete fuzzy preference relation. Subsequently, a complete ordinal score vector is proposed to describe the preference ranking over different states based on the complete fuzzy preference relation. Moreover, in the framework of the inverse approach of GMCR, some mathematical models with the least constraint conditions are proposed to obtain all the required preference relations for opponent DM, which are required to make a given state be stable under four basic stability definitions. Finally, WEF nexus evaluation in Shandong province is illustrated to demonstrate the usefulness of the inverse approach of GMCR with the incomplete fuzzy preference relations.

Performance evaluation of Asian major cruise terminals

Compared to cargo terminals, performance evaluation of cruise terminals sits in a backseat in the current port performance studies, particularly in the Northeast Asia. This paper aims to evaluate the performance of major cruise terminals in Asia, taking into account the important dimensions and criteria influencing cruise terminal development, and using primary data by experts' responses to a questionnaire survey. Complementary data are also acquired by field trips and interviews to find the hidden information that is not revealed from the set questions in the questionnaire survey. To tackle the multiplicity and uncertainty in the collected data, the hybrid of Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Consistent Fuzzy Preference Relation (CFPR) is employed to evaluate cruise terminal performance and highlight visual illustration of the test results. The findings provide useful insights for guiding cruise lines’ terminal selection and for aiding cruise terminals to benchmark their performance to effectively improve their service and maximize their overall performance.

A prioritization approach of non-reciprocal fuzzy preference relations and its extension

In decision making problems, preference relations are the widely used tools to simulate the opinions of decision makers (DMs). It is important to elicit priorities from a preference relation in order to reach an optimal solution. In this paper, we propose an optimization-based method to derive the priority vector from a fuzzy preference relation (FPR) without additively reciprocal property (ARP). First, the concept of non-reciprocal fuzzy preference relations (NrFPRs) is used to characterize the case of FPRs without ARP. An interval additive reciprocal matrix (IARM) is decomposed into two NrFPRs. Second, the variance similarity measure is proposed to quantify the similarity degree of two column/row vectors of NrFPRs. A double variance consistency index is constructed to measure the inconsistency degree of NrFPRs. An optimization-based model is proposed to elicit the priority vector from NrFPRs. Some interesting properties are addressed to show the effectiveness of the proposed approach. Third, the proposed prioritization method is extended to derive priorities from IARMs. Some comparisons with the existing prioritization methods of IARMs are reported using numerical examples. The observations reveal that the uncertainty exhibiting in IARMs can be captured by NrFPRs. Some decision-making models with IARMs could be revisited using the proposed method for NrFPRs.

Ranking Load in Microgrid System Based on the Priority Weight Calculation of Power Supply

This paper proposes a method of ranking load in Microgird system based on the calculation of priority weights on the continuity of power supply of the loads. The proposed method applies the covariance matrix of the criterion layer to determine values of each criterion. The fuzzy preference relation matrix is used to replace the pairwise comparison matrix of the scheme layer. The weight of the criterion layer and the scheme layer are combined to get the final weights of each load bus. This solves the problem of consistency factor when evaluating loads from experts and Microgrid system operators. The calculation of priority weighting on the continuity of power supply for loads supports system operators to be proactive in planning load shedding or load shedding due to system failures, thereby minimizing damage to the system and electricity customers. The proposed method is calculated on the diagram of the IEEE 16 bus Microgrid system with six sources and eight loads.

Decision-making models based on satisfaction degree with incomplete hesitant fuzzy preference relation

To address the situation where incomplete hesitant fuzzy preference relation (IHFPR) is necessary, this paper develops decision-making models taking into account decision makers’ satisfaction degree. First, the consistency measures, respectively, from the perspectives of additive and multiplicative consistent IHFPR are defined, which is based on the relationships of the IHPFRs and their corresponding priority weight vector. Second, two decision-making models are developed, respectively, in view of the proposed additive and multiplicative consistency measures. The main characteristic of the constructed models is they taking into account the decision makers’ satisfaction degree. The objective functions of the models are developed by maximizing the parameter of the satisfaction degree. Third, a square programming model is developed to obtain the decision makers’ weights by utilizing the optimal priority weight vectors information, the solution of the model is obtained by solving the partial derivatives of Lagrange function. Finally, a procedure for multi-criteria decision-making (MCDM) problems with IHFPRs is given, and an illustrative example in conjunction with comparative analysis is used to demonstrate the proposed models are feasible and efficiency for practical MCDM problems.

A consensus building model in group decision making with non-reciprocal fuzzy preference relations

Group decision making (GDM) is a wisdom extracting process where a group of decision makers (DMs) could reach a consensus on the optimal solution to the choice problem with a finite set of alternatives. This paper reports a consensus model in GDM, where the opinions of experts are expressed as fuzzy preference relations (FPRs) without additively reciprocal property to cope with the existing uncertainty. The concept of non-reciprocal fuzzy preference relations (NrFPRs) is proposed to capture the considered situation. A novel additive consistency index is constructed to quantify the inconsistency degree of NrFPRs using the relationship of two column/row vectors. An optimization model is constructed, where a new fitness function is proposed by considering the consistency degrees of NrFPRs and the consensus level of a group of experts. A novel concept of acceptable consensus standard is proposed to characterize the acceptance of the consensus process. The particle swarm optimization (PSO) algorithm is utilized to solve the constructed optimization problem. As compared to the existing models, numerical results show that the proposed model can be used to effectively reach an optimal solution to a GDM problem with NrFPRs.

Cognitive fuzzy preference relations and its applications in decision-making

Cognitive fuzzy preference relations and its applications in decision-making

A novel group decision making method for interval-valued pythagorean fuzzy preference relations

Interval-valued Pythagorean fuzzy preference relation (IVPFPR) plays an important role in representing the complex and uncertain information. The application of IVPFPRs gives better solutions in group decision making (GDM). In this paper, we investigate a new method to solve GDM problems with IVPFPRs. Firstly, novel multiplicative consistency and consensus measures are proposed. Subsequently, the procedure for improving consistency and consensus levels are put forward to ensure that every individual IVPFPR is of acceptable multiplicative consistency and consensus simultaneously. In the context of minimizing the deviations between the individual and collective IVPFPRs, the objective experts’ weights are decided according to the optimization model and the aggregated IVPFPR is derived. Afterwards, a programming model is built to derive the normalized Pythagorean fuzzy priority weights, then the priority weights of alternatives are identified as well. An algorithm for GDM method with IVPFPRs is completed. Finally, an example is cited and comparative analyses with previous approaches are conducted to illustrate the applicability and effectiveness of the proposed method.

Group decision making method with hesitant fuzzy preference relations based on additive consistency and consensus

To address the situation where the multi-criteria decision making (MCDM) has problems with hesitant fuzzy preference relations (HFPRs), this paper develops a group decision making method considering the additive consistency and consensus simultaneously. First, a new normalized method for HFPRs is developed to address the situation where the evaluation information has different number of elements. Second, for improving the unacceptable consistent HFPRs, an algorithm is designed to derive acceptable consistent HFPRs. The main characteristic of the design algorithm is that the values that need to be revised are identified first, and then design the local adjustment process. Third, an algorithm is developed to obtain a group of normalized HFPRs (NHFPRs), considering the additive consistency of HFPRs. Fourth, for improving the individual consistency and group consensus simultaneously, an algorithm is designed to obtain a group of HFPRs with acceptable consistency and consensus. Finally, the method of determining the decision makers’ weights and a procedure for MCDM problems with HFPRs are given. An illustrative example in conjunction with comparative analysis is used to demonstrate the proposed method which is feasible and efficient for practical MCDM problems.

RG-SKY: A fuzzy group skyline relaxation for combinatorial decision making

Skyline queries were recently expanded to group decision making to meet complex real-life needs encountered in many modern application domains that does not only require analyzing individual points but also groups of points. Group skyline aims at retrieving groups that are not dominated by any other group of the same size in the sense of a group-dominance relationship. It may often happens that this kind of dominance leads to only a small number of non-dominated groups which could be insufficient for the decision maker. In this paper, we propose to extend group skyline dominance by making it more demanding so that several groups leave incomparable. Then, the original group skyline will be enlarged by some interesting groups that are not much dominated by any other group. The key element of this relaxation is a particular fuzzy preference relation, named ?much preferred?, conveniently chosen. Furthermore, algorithms to compute the relaxed group skyline are proposed. Finally, a set of experiments are conducted on real, synthetic and generated data. Such experiments show that our proposal can really improve the decision process and satisfy user queries, insure reliability and decision quality.

Evaluation of associated credit risk in supply chain based on trade credit risk contagion

The evaluation of associated credit risk in supply chain is a difficult problem in the current credit risk management practice. Based on graph theory and fuzzy preference theory, we put forward a new evaluation method of associated credit risk in the supply chain. First, the credit risk of enterprises in supply chain are divided into “self-own credit risk” and “credit risk contagion”. Second, the indicator system of self-own credit risk evaluation of enterprises in the supply chain is designed, and the fuzzy comparative judgment matrix of self-own credit risk evaluation indicator is obtained by using fuzzy preference relation, and the evaluation basic model of “self-own credit risk” is established. On the basis, the evaluation derivative model of “credit risk contagion” is established. Furthermore, by integrating the two kinds of evaluation results, the credit risk of enterprises in supply chain is evaluated comprehensively, and the contagion effect of associated credit risk in supply chain based on trade credit risk contagion (TCRC) is revealed.

Cubic Pythagorean Fuzzy Graphs

The purpose of the study is to explore graph theory based on cubic Pythagorean fuzzy sets. The concept of cubic Pythagorean fuzzy graphs (CuPFGs) is introduced in this research work. In addition, we define certain fundamental operations on CuPFGs including semistrong product, lexicographical product, and symmetric difference of two CuPFGs and demonstrate some of their key characteristics. Meanwhile, to investigate the preference of decision makers, cubic Pythagorean fuzzy preference relation is defined. Moreover, through a practical example, the applicability of our proposed work in multicriteria decision‐making is described, and to clarify the organization of the proposed method, a frame diagram is presented.

Group Decision-Making Method with Incomplete Intuitionistic Fuzzy Preference Relations Based on a Generalized Multiplicative Consistent Concept

Based on the analyses of existing preference group decision-making (PGDM) methods with intuitionistic fuzzy preference relations (IFPRs), we present a new PGDM framework with incomplete IFPRs. A generalized multiplicative consistent for IFPRs is defined, and a mathematical programming model is constructed to supplement the missing values in incomplete IFPRs. Moreover, in this study, another mathematical programming model is constructed to improve the consistency level of unacceptably multiplicative consistent IFPRs. For group decisionmaking (GDM) with incomplete IFPRs, three reliable sources inuencing the weights of experts are identified. Subsequently, a method for determining the weights of experts is developed by simultaneously considering three reliable sources. Furthermore, a targeted consensus process (CPR) is developed in this study with reference to the actual situation of the consensus level of each IFPR. Meanwhile, in response to the proposed multiplicative consistency definition , a novel method for determining the optimal priority weights of alternatives is redefined. Lastly, based on the above theory, a novel GDM method with incomplete IFPRs is developed, and the comparative and sensitivity analysis results demonstrate the utility and superiority of this work.

A signed distance based ranking approach with unknown fuzzy priority vectors for medical diagnosis involving interval type-2 trapezoidal pythagorean fuzzy preference relations

In many of our real life problems, we often come across situations where there is no information about the priority weights which make it difficult to analyze the objects under consideration. Instead of employing simple fuzzy sets, “interval type-2 trapezoidal pythagorean fuzzy preference relations (IT2TrPFPRs)” can be used which have better representational power and ability to cope with uncertain situations. The approach discussed in this article is an effective tool for managing multiple criteria group decision-making situations with completely unknown priority weights modeled as IT2TrPFPRs. To aggregate the opinion of multiple decision-makers, a hybrid averaging operation based on weighted averaging and ordered weighted averaging (OWA) operations is employed for a collective decision environment. To calculate the fuzzy priority weight vectors in case of completely unknown environment, we construct a non-linear optimization model. An integrated optimization model based on a new signed distance-based closeness coefficients approach is employed to determine the priority ranking of alternatives. Feasibility of the proposed technique is discussed with an implementation of patient centered medicine system for choosing the appropriate treatment method. Moreover, a comparative investigation with previous approaches is conducted to demonstrate the effectiveness of the given approach.

Evaluation of Breakthrough Technological Innovation Capability of Petroleum Equipment Manufacturing Enterprises Based on the CFPR‐TIFNs Model

“Internet + innovation” is a new emerging paradigm for breakthrough technological innovation, which is more complex than traditional scenarios because “Internet +” has completely overturned the acquisition of creativity and knowledge. This article aims to evaluate the breakthrough technological innovation capability (BTIC) of petroleum equipment manufacturing enterprises under the new paradigm of “Internet + innovation.” This article constructs an index system that can reflect the characteristics of “breakthrough” and “Internet +” in five dimensions. Due to the difficulties of conducting quantitative measurements for the novel index system, a hybrid evaluation model based on a consistent fuzzy preference relationship (CFPR) and triangular intuitionistic fuzzy numbers (TIFNs) is put forward for the evaluation of BTIC. The main contribution of this article is to propose an index system and model for the evaluation of BTIC under the “Internet + innovation” paradigm, which is conducive to improving the quality of breakthrough technological innovation activities under more complex scenarios. The CFPR‐TIFNs model can not only effectively calculate the BTIC of petroleum equipment manufacturing enterprises but also deeply analyze the influencing factors for their relatively weak technological innovation capability. This article provides a new methodology to solve problems in a more complex technological innovation system.

Generalization and ranking of fuzzy numbers by relative preference relation

We define 2n+1 and 2n fuzzy numbers, which generalize triangular and trapezoidal fuzzy numbers, respectively. Then, we extend the fuzzy preference relation and relative preference relation to rank 2n+1 and 2n fuzzy numbers. When the data is representable in terms of 2n+1 fuzzy number, we generalize the FMCDM (fuzzy multi-criteria decision making) model constructed with TOPSIS and relative preference relation. Lastly, we give an example from telecommunications to present the proposed FMCDM model and validate the results obtained.