Interval-valued Hesitant Fuzzy Research Articles

A novel approach for managing change in warehouse processes via integration of business process re-engineering and interval-valued hesitant fuzzy DEMATEL

PurposeThe main purpose of this study is to present a new approach to managing process changes in uncertain conditions. The proposed approach is applied in one of the largest production companies in Turkey to manage the changes in their warehouse processes which formed after the merger.Design/methodology/approachIn the model, interval-valued hesitant fuzzy the decision-making trial and evaluation laboratory (IVHF-DEMATEL) methodology is integrated into one of the most used BPR tools, change matrix. The main focus of the proposed model is to increase both flexibility and applicability in uncertain conditions. Thus, while the change matrix enables companies to be agile and responsive to changes, IVHF-DEMATEL provides a better way to continuously evaluate and determine critical processes, and strategies to align with evolving conditions.FindingsInitial analysis revealed two major problems, the slowness of shipments caused by the increase in costs and the confusion in the organizational structure. However, the conventional methods fall short of effectively determination of critical objectives in terms of dealing with uncertainty. Therefore, a comprehensive roadmap for managing the change is developed with the integration of IVHF-DEMATEL and change matrix so that a successful transition is achieved.Originality/valueIt is believed that the study will contribute to the existing literature by providing a novel approach in which the IVHF-DEMATEL methodology is integrated into the change matrix. Also, the study provides a guideline for practical applications by presenting a step-by-step implementation of the model.

Achieving strategic excellence: Integrating balanced scorecard and IVHF-DEMATEL for strategic management of a medical device SME

Nowadays, strategic planning and the establishment of long-term objectives are pivotal for companies from diverse economies based on geographical location, demographic distribution, financial challenges, and distinct customer portfolios. Defining strategic goals is vital more than ever however requires acceptance, adoption, and execution by the entire workforce. In this sense, this paper presents an integrated methodology that combines the Balanced Scorecard and the Interval-Valued Hesitant Fuzzy DEMATEL approaches to enhance strategic management in a Medical Device SME located in Istanbul-Turkey. The proposed approach aims to provide a holistic perspective on organizational performance and facilitate decision-making processes for sustainable growth and competitive advantage.

An Improved Interval-Valued Hesitant Fuzzy Weighted Geometric Operator for Multi-Criterion Decision-Making

In this paper, an improved interval-valued hesitant fuzzy weighted geometric (IIVHFWG) operator for multi-criterion decision-making is proposed. This operator is free of the limitations of the existing interval-valued hesitant fuzzy weighted average operator, interval-valued hesitant fuzzy weighted geometric operator, generalized interval-valued hesitant fuzzy weighted geometric operator, interval-valued hesitant fuzzy Hammer weighted average operator, and interval-valued hesitant fuzzy Hammer weighted geometric operator, which are prone to being influenced by extreme values. Based on the proposed IIVHFWG operator, a new method to solve the multi-criterion decisionmaking problems with interval-valued hesitant fuzzy elements is presented. Several numerical examples together with comparisons are introduced to demonstrate the effectiveness and advantages of this method.

Undergraduate Teaching Audit and Evaluation Using an Extended ORESTE Method with Interval-Valued Hesitant Fuzzy Linguistic Sets

Undergraduate teaching audit and evaluation (UTAE) plays a substantial role in the teaching quality assurance and monitoring of universities. It achieves the goal of selecting the best university for promoting the quality of higher education in China. Generally, the UTAE is a complex decision-making problem by considering competing evaluation criteria. Moreover, the evaluation information on the teaching quality of universities is often ambiguous and hesitant because of the vagueness existing in human judgments. Previous studies on UTAE have paid subtle attention towards the managing of linguistic expressions and the performance priority of universities. The interval-valued hesitant fuzzy linguistic sets (IVHFLSs) can effectively describe uncertainty, hesitancy, and inconsistency inherent in decision-making process. The ORESTE (organísation, rangement et Synthèse de données relarionnelles, in French) is a new outranking decision-making method which can show detailed distinctions between alternatives. Therefore, in this study, we propose a new UTAE approach based on the VHFLSs and ORESTE method to resolve the prioritization of universities for selecting the optimal university to benchmark. Specifically, the presented method handles the hesitant and uncertain linguistic expressions of experts by adopting the IVHFLSs and determines the ranking of universities with an extended ORESTE approach. Finally, a practical UTAE example illustrates the feasibility the proposed approach and a comparison analysis provides grounding for the superiority of the integrated approach. When the obtained results are evaluated, U2 has been determined as the best university. The results indicate the good performance of the proposed UTAE approach in evaluating and improving the teaching quality of universities.

The Generalized Similarity Measures Based on Complex Interval-Valued Hesitant Fuzzy Sets and Their Applications in Pattern Recognition and Medical Diagnosis

The Generalized Similarity Measures Based on Complex Interval-Valued Hesitant Fuzzy Sets and Their Applications in Pattern Recognition and Medical Diagnosis

A New Approach to Correspondence Analysis Based on Interval-Valued Hesitant Fuzzy Sets

Correspondence Analysis (CA), a multivariate statistical technique, allows a visual representation of the association between categorical variables through a contingency table consisting of frequencies representing the existence of relationships. Despite being a widely used statistical technique, the classical CA is not able to demonstrate the uncertainty in real-life problems. To address this issue, a new Interval-valued Hesitant Fuzzy CA approach is proposed to represent the uncertainty caused by human doubt. Due to the nature of operations defined on Hesitant Fuzzy Sets, it is hard to integrate the fuzzy calculations directly into the classical CA. Thus, a new hesitant expected value method is proposed to reveal the independence between two categorical variables. As the output of the proposed approach, an interval-valued hesitant fuzzy correspondence map consisting of rectangles of different sizes representing the amount of the hesitancy is constructed. The applicability of the proposed approach is demonstrated by a simple but effective illustrative example.

An Efficiency-Based Interactive Dynamic Technique with Interval-Valued Hesitant Fuzzy Constraint Cone for Rescue Route Planning

An Efficiency-Based Interactive Dynamic Technique with Interval-Valued Hesitant Fuzzy Constraint Cone for Rescue Route Planning

Selection of an alternative based on interval-valued hesitant picture fuzzy sets

Motivated by interval-valued hesitant fuzzy sets (IVHFSs) and picture fuzzy sets (PcFSs), a notion of interval-valued hesitant picture fuzzy sets (IVHPcFSs) is presented in this article. The concept of IVHPcFSs is put forward and some operational rules are developed to deal with it. The cosine similarity measures (SMs) are modified for IVHPcFSs to deal with interval-valued hesitant picture fuzzy (IVHPcF) data and the linear programming (LP) methodology is used to find out the criteria’s weights. A multiple criteria decision making (MCDM) approach is then developed to tackle the vague and ambiguous information involved in MCDM problems under the framework of IVHPcFSs. For the validation and strengthen of the proposed MCDM approach a practical example is put forward to select the educational expert at the end.

An extended TDM method under probabilistic interval-valued hesitant fuzzy environment for stock selection.

Generally, in real decision-making, all the pieces of information are used to find the optimal alternatives. However, in many cases, the decision-makers (DMs) only want “how good/bad a thing can become.” One possibility is to classify the alternatives based on minimum (tail) information instead of using all the data to select the optimal options. By considering the opportunity, we first introduce the value at risk (VaR), which is used in the financial field, and the probabilistic interval-valued hesitant fuzzy set (PIVHFS), which is the generalization of the probabilistic hesitant fuzzy set (PHFS). Second, deemed value at risk (DVaR) and reckoned value at risk (RVaR) are proposed to measure the tail information under the probabilistic interval-valued hesitant fuzzy (PIVHF) environment. We proved that RVaR is more suitable than DVaR to differentiate the PIVHFEs with example. After that, a novel complete group decision-making model with PIVHFS is put forward. This study aims to determine the most appropriate alternative using only tail information under the PIVHF environment. Finally, the proposed methods’ practicality and effectiveness are tested using a stock selection example by selecting the ideal stock for four recently enrolled stocks in China. By using the novel group decision-making model under the environment of PIVHFS, we see that the best stock is E4 when the distributors focus on the criteria against 10% certainty degree and E1 is the best against the degree of 20%, 30%, 40% and 50% using the DVaR method. On the other hand when RVaR method is used then the best alternative is E4 and the worst is E2 against the different certainty degrees. Furthermore, a comparative analysis with the existing process is presented under the PHF environment to illustrate the effectiveness of the presented approaches.

An effective similarity measurement under epistemic uncertainty

The epistemic uncertainty stems from the lack of knowledge and it can be reduced when the knowledge increases. Such interpretation works well with data represented as a set of possible states and therefore, multivalued similarity measures. Unfortunately, set-valued extensions of similarity measures are not computationally feasible even when the data is finite. Measures with properties that allow efficient calculation of their extensions, need to be found. Analysis of various similarity measures indicated logic-based (additive) measures as an excellent candidate. Their unique properties are discussed and efficient algorithms for computing set-valued extensions are given. The work presents results related to various classes of fuzzy set families: general ones, intervals of fuzzy sets, and their finite sums. The first case is related to the concept of the Fuzzy Membership Function Family, the second corresponds to the Interval-Valued Fuzzy Sets, while the third class is equivalent to the concept of Typical Interval-Valued Hesitant Fuzzy Sets.

Interval-Valued Hesitant Fuzzy TOPSIS Decision Making Method with Group Consistency

Interval-Valued Hesitant Fuzzy TOPSIS Decision Making Method with Group Consistency

Group decision on the evaluation of outsourcing for information systems employing interval-valued hesitant fuzzy modeling

Organizations design procedures for information systems (ISs) management by outsourcing operations that could incorporate applications, assets, and other resources related to the IS-activities. This paper proposes a novel group decision-making approach in light of cooperative choice investigations under interval-valued hesitant fuzzy (IVHF)-uncertainty to evaluate IS-outsourcing candidates. The IVHFSs could help the IS-decision makers (DMs) by appointing interval-valued membership degrees for IS-outsourcing' exercises among the conflict factors by regarding a set to the margin of errors. For a reason, preferences judgments of IS-experts are communicated by linguistic terms, which they are changed over to IVHF-elements. In the proposed model, the weights of evaluation factors or criteria and experts are figured in light of IVHF-preference evaluation technique and IVHF-utility degree strategy. Then, criteria and IS-DMs' weights are given in the method of proposed assessing and ranking approach to decrease the errors. Furthermore, the judgments are amassed in the last step of the presented model to prevent the loss of information. At last, the proposed IVHF-group decision model is implemented to a case study to demonstrate its suitability along with comparative analysis from different aspects.

A method to multi-attribute decision-making based on interval-valued q-rung dual hesitant linguistic Maclaurin symmetric mean operators

The aim of this paper is to propose a new multi-attribute decision-making (MADM) method to rank all feasible alternatives in complex decision-making scenarios and determine the optimal one. To this end, we first propose the notion of interval-valued q-rung dual hesitant linguistic sets (IVq-RDHLSs) by combining interval-valued q-rung dual hesitant fuzzy (IVq-RDHF) sets with linguistic terms set. The proposed IVq-RDHLSs utilize IVq-RDHF membership and non-membership degrees to assess linguistic terms, so that they can fully express decision-makers’ evaluation information. Additionally, some related concepts such as the operational rules, score and accuracy functions, and ranking method of IVq-RDHLSs are presented. Considering the good performance of the classical Maclaurin symmetric mean (MSM) in integrating fuzzy information, we further generalize MSM into IVq-RDHLSs to propose the interval-valued q-rung dual hesitant linguistic MSM operator, the interval-valued q-rung dual hesitant linguistic dual MSM operator, as well as their weighted forms. Afterwards, we study the applications of IVq-RDHLSs and their aggregation operators in decision-making and propose a new MADM method. Some real decision-making problems in daily life are employed to prove the rightness of the proposed method. We also attempt to demonstrate the advantages and superiorities of our proposed method through comparing with some other methods in this paper.

Ancient Chinese Character Image Segmentation Based on Interval-Valued Hesitant Fuzzy Set

To address the low segmentation accuracy caused by the rich glyph styles of ancient Chinese characters and the complex layout of ancient Chinese books, which affects the retrieval and recognition results, an algorithm for the layout image analysis of ancient Chinese books and Chinese character image segmentation is proposed. The initial segmentation results were obtained through the projection method of the layout of ancient Chinese books, and the connected component analysis of the above results was carried out to determine the rough divided blocks of under-segmentation and over-segmentation. Considering under-segmentation of adhesive Chinese characters, the improved K-means clustering method was used to segment adhesive blocks to obtain single-character images. To address the over-segmentation of character components separation, a method based on interval-valued hesitant fuzzy set is proposed. This method analyzed the features of the connected component in the block, characterized the over-segmentation connected component. The hesitant fuzzy distances between other connected components and the standard merge evaluation interval number were calculated in sequence. The connected component with the smallest distance was preferentially merged with the over-segmentation connected component until no over-segmentation connected component remained in the block. The experimental segmentation accuracy was 89.94%.

A Prospect Theory Based Probabilistic Interval-Valued Hesitant Fuzzy Sets with Interval Reference Point and Incomplete Weight Information for Multi-Criteria Decision Making

In this paper, we introduce probabilistic interval-valued hesitant fuzzy sets (PIVHFS) and probabilistic interval-valued hesitant fuzzy elements (PIVHFE), then define correlation operations. At the same time, the hybrid distance measures of PIVHFE and interval fuzzy number are defined, and the hybrid distance measures of two PIVHFEs are also defined. Considering the psychological behavior of decision makers, we use interval fuzzy number decision makers' psychological interval reference points to calculate the gains and losses of evaluation value. Furthermore, based on incomplete weight information, the grey relational analysis (GRA) method is extended to calculate the optimal weight. Then, based on the traditional TOPSIS method, the decision-making method is proposed. Finally, the proposed method is applied to an example, and the sensitivity analysis and stability analysis of the proposed method are also discussed.

Reconfiguring IVHF-TOPSIS decision making method with parameterized reference solutions and a novel distance for corporate carbon performance evaluation

The hesitant and imprecise uncertainties widely exist in real decision-making problems. For solving the class of problems, this work is aimed at reconfiguring a novel method under the TOPSIS framework to solve the general uncertain decision problem that with both the ratings of alternatives and weights of criteria represented by interval-valued hesitant fuzzy (IVHF) information. Three novelties are proposed to support the reconfigured method. First, a parameterized approach for generating apt positive and negative IVHF reference solutions is proposed, which permits decision makers (DMs) to express their different aspiration strengths for fitting complex and uncertain decision scenarios and situations. Second, a novel distance for IVHF elements is constructed based on the modification of Wave-Hedges measurement to address the defects of the previous hesitant distances and to measure the separations of alternatives in TOPSIS. Third, a nine-step solution procedure of IVHF-TOSPSIS method is reconfigured to solve effectively the general problem that the ratings and weights are expressed with IVHF information. Finally, the reconfigured method is exploited to settle the carbon performance evaluation of industrial firms and some sensitivity and comparison analyses are conducted to validate the proposed method.

Interval-valued hesitant fuzzy multi-granularity three-way decisions in consensus processes with applications to multi-attribute group decision making

Multi-attribute group decision making (MAGDM) is a common activity for multi-variable complicated decision making situations by integrating collective wisdom. Aiming at fusing granular computing with three-way decisions (3WD) to study scheme synthesis and analysis of solution space, multi-granularity three-way decisions (MG-3WD) provide multi-dimension problem solving methods for MAGDM problems. By using MG-3WD frameworks, this paper intends to study viable strategies of processing consensus and conflicting opinions provided by different decision makers in the interval-valued hesitant fuzzy (IVHF) MAGDM problem. More specifically, after reviewing the relevant literature, four kinds of IVHF multigranulation decision-theoretic rough sets (MG-DTRSs) over two universes are proposed according to different risk appetites of experts firstly. Then, we explore some fundamental propositions of newly proposed models. Afterwards, solutions to MAGDM problems in the context of mergers and acquisitions (M&A) target selections by using the presented IVHF MG-DTRSs over two universes are constructed. At last, a M&A target selection case study, along with a sensitivity analysis and a comparative analysis, is applied to illustrate the established decision making approaches.

Interval-Valued Dual Hesitant Fuzzy Hamacher Aggregation Operators for Multiple Attribute Decision Making

AbstractAs an generalization of hesitant fuzzy set, interval-valued hesitant fuzzy set and dual hesitant fuzzy set, interval-valued dual hesitant fuzzy set has been proposed and applied in multiple attribute decision making. Hamacher t-norm and t-conorm is an generalization of algebraic and Einstein t-norms and t-conorms. In order to combine interval-valued dual hesitant fuzzy aggregation operators with Hamacher t-norm and t-conorm. We first introduced some new Hamacher operation rules for interval-valued dual hesitant fuzzy elements. Then, several interval-valued dual hesitant fuzzy Hamacher aggregation operators are presented, some desirable properties and their special cases are studied. Further, a new multiple attribute decision making method with these operators is given, and an numerical example is provided to demonstrate that the developed approach is both valid and practical.

Multiple Criteria Decision Making Based on Probabilistic Interval-Valued Hesitant Fuzzy Sets by Using LP Methodology

Probabilistic interval-valued hesitant fuzzy sets (PIVHFSs) are an extension of interval-valued hesitant fuzzy sets (IVHFSs) in which each hesitant interval value is considered along with its occurrence probability. These assigned probabilities give more details about the level of agreeness or disagreeness. PIVHFSs describe the belonging degrees in the form of interval along with probabilities and thereby provide more information and can help the decision makers (DMs) to obtain precise, rational, and consistent decision consequences than IVHFSs, as the correspondence of unpredictability and inaccuracy broadly presents in real life problems due to which experts are confused to assign the weights to the criteria. In order to cope with this problem, we construct the linear programming (LP) methodology to find the exact values of the weights for the criteria. Furthermore these weights are employed in the aggregation operators of PIVHFSs recently developed. Finally, the LP methodology and the actions are then applied on a certain multiple criteria decision making (MCDM) problem and a comparative analysis is given at the end.

Criteria Assessment in Sustainable Macromanagement of Housing Provision Problem by a Multi-phase Decision Approach with DEMATEL and Dynamic Uncertainty

Macromanagement of housing provision based on sustainable development characteristics may be required for applying some different strategies to ensure the housing preparation regarding economic, environmental, and social competencies. In this regard, there are some challenges, including defining the criteria in a hierarchical structure to check the problem in all aspects, computing experts’ weights for reducing judgments’ errors, computing interdependencies effects among criteria to increase the precise of criteria weight determination, dynamic criteria’ weights evaluation to analyze the criteria importance in some different periods, and uncertainty modeling of criteria weights computations in the research area, in which recent studies did not focus them simultaneously. To address the issue, this study proposes a multi-phase weighting approach by combining the collective index and decision-making trial and evaluation laboratory (DEMATEL) methodologies via dynamic interval-valued hesitant fuzzy sets (DIVHFSs) theory. Considering dynamic uncertainty is needed for covering the incomplete information in various periods because the significance and impact of evaluation criteria may be changed in each period. On the other hand, DIVHFSs theory could assist the experts by assigning some dynamic interval-valued hesitant fuzzy (DIVHF)-membership degrees for an element under a set to decrease the errors. In proposed DIVHF-collective index method, criteria local weights are obtained based on correlation and standard deviation approaches under a DIVHF-environment. In addition, the extended DEMATEL methodology is prepared for taking account of hierarchical structure and criteria interdependencies relations. Moreover, a novel DIVHF-utility degree method is presented to compute the weight of each expert. Then, experts’ weights are taken into account in the presented multi-phase weighting approach. In addition, the DIVHF-multi-phase weighting approach based on collective index and DEMATEL (DIVHF-MPW-CI–DEMATEL) methodologies is proposed via a last aggregation approach to keep away from the data loss. Meanwhile, some needed operations on DIVHFS theory for presenting DIVHF-MPW-CI–DEMATEL methodology are extended. Finally, a real case study about the determining criteria’ weights for strategy selection in macromanagement of housing provision problem based on sustainable development properties is prepared to indicate the feasibility and applicability of proposed DIVHF-MPW-CI–DEMATELs approach.