Hesitant Fuzzy Linguistic Term Research Articles

Ship collision risk assessment: A multi-criteria decision-making framework based on Dempster–Shafer evidence theory

Ship collision risk modelling is dominated by the utilization of the experts’ experiential insights and knowledge. To construct an effective assessment model for evaluating potential ship collision risks grounded in expert judgments, this paper introduces a novel multi-criteria decision-making framework based on Dempster–Shafer evidence theory (DSET). Firstly, a novel index-based assessment system is provided to generate the potential collision risk value of ships based on automatic identification system (AIS) data, which encompasses ship condition parameters, deviation coefficient, and frequency coefficient. Further, to generate the weights of these indicators, a framework to extract expert opinions is provided, which includes two parts: a) converting experts’ opinions into D-S framework through hesitant fuzzy linguistic terms and b) generating experts’ opinions using improved DSET. Regarding part a, a methodology is proposed to evaluate the trustworthiness of experts by considering both the level of uncertainty in their opinions and the degree of similarity between their viewpoints. Concerning part b, strategies are integrated to ascertain the most suitable weight for the basic probability assignment, thereby mitigating conflicts within the evidence. Finally, based on the collected AIS data, the proposed framework has been applied to six of the world's busiest waterways: (1) the Strait of Malacca, (2) the English Channel, (3) the Panama Canal, (4) the Suez Canal, (5) the Danish Straits, and (6) the Strait of Hormuz. Subsequently, comprehensive analytical results and recommendations are furnished, such as wide and smooth channels showing elevated risk.

A Novel Early Warning Method for Handling Non-Homogeneous Information

Early warnings are an indispensable part of emergency management, which is a powerful way to eliminate or reduce the negative impacts caused by emergencies in advance. Early warning problems have been discussed from different perspectives and have obtained fruitful results. Information plays a critical role in all kinds of decision problems, with no exception for the early warning problem. There are various information types related to emergencies in real-world situations; however, existing early warning studies only considered a single information type, which might not describe the problem properly and comprehensively. To enrich existing early warning studies, a novel early warning method considering non-homogeneous information together with experts’ hesitation is proposed, in which numerical values, interval values, linguistic terms, and hesitant fuzzy linguistic terms are considered. To facilitate the computations with non-homogeneous information, a transformation process needs to be conducted. On such a basis, a fuzzy TOPSIS method based on alpha-level sets is employed to handle the transformed fuzzy information due to its superiority in obtaining information and its capacity to contain as much information as possible during the early warning process. Additionally, two different options are provided to analyze the status and tendency of early warning objects. Finally, an illustrative example about early warnings about landslides and a related comparison are conducted to demonstrate the novelty, superiority, and feasibility and validity of the proposed method.

Multi-criteria evaluation of smart product-service design concept under hesitant fuzzy linguistic environment: A novel cloud envelopment analysis approach

Design concept evaluation is an essential but challenging task in smart product service system (SPSS) design. It is critical to select the design concept with better smart experience to ensure the success of SPSS design. Meanwhile, SPSS design concept evaluation is an expert knowledge-based decision process with scant data and a short time window under a hesitant, ambiguous, and subjective environment, which would lead to low decision-making efficiency and inaccurate evaluation results. Hence, this paper proposes a novel cloud envelopment analysis method to evaluate SPSS design concept with respect to smart experience criteria. The proposed method integrates hesitant fuzzy linguistic terms (HFLTs), cloud model and data envelopment analysis (DEA) method to accurately evaluate SPSS design concepts with handling the hesitancy, fuzziness and randomness in qualitative and subjective information. A hybrid model of normal cloud and trapezium cloud is used to quantify hybrid-length HFLT variables to avoid decision information loss and distortion. A novel cloud non-linear programming model is constructed to extend the classical DEA into cloud environment. Finally, a case study and some comparison analyses illustrate the effectiveness and advantages of proposed method.

A novel weight‐derived method and its application in graduate students' physical health assessment

In multiattribute decision making, the analytic network process (ANP) is an important methodology to derive the subjective weights of attributes when the dependence and feedback relations exist between attributes, and the number of attributes should be no more than seven in a comparison matrix. To reduce the dimensions of attributes, we propose a hybrid hesitant fuzzy linguistic factor analysis method to cluster the attributes into main factors. The method takes multiple forms of decision-making information into consideration, such as single linguistic terms, hesitant fuzzy linguistic terms, and numeric values. Meanwhile, the objective weights of the main factors are obtained as well. As for the subjective weights of main factors, the incomplete probabilistic linguistic ANP is developed after improving the incomplete probabilistic linguistic preference relation with multiplicative consistency. At last, the final weights of the main factors are calculated by combining the objective and subjective weights. A questionnaire survey about assessing the weights of the main factors influencing graduate students' physical health is designed to explain the application of the proposed methodology. To sum up, the main importance and contributions of this study are as following: (1) developing a hybrid hesitant fuzzy linguistic factor analysis method and incomplete probabilistic linguistic ANP, (2) proposing a novel weight-derived method from both objective and subjective perspectives, and (3) applying it to graduate students' physical health assessment.

Solar energy plant project selection with AHP decision-making method based on hesitant fuzzy linguistic evaluation

Increased energy demand is expected to be met by reliable and continuous energy sources. Renewable energy which is obtained from nature and can continuously reload itself from natural sources is a new generation energy type. The sun, which is the main source of renewable energies and produces heat and electricity by direct and indirect methods, is an important renewable energy source. The installation of solar energy systems takes place under the basic technical, economic and political factors. Alternative solar energy plant projects are evaluated linguistically under the main criteria based on the knowledge and experience of the experts. Hesitant fuzzy linguistic terms are used to incorporate the uncertain and hesitant expressions into the decision-making process. The decision-making process that takes place with hesitant linguistic expressions in multiple sub-criteria is based on the AHP model. The inclusion of hesitant statements in the decision-making process with the AHP model enables more realistic choices among the alternatives. System technology (0.18), energy policy (0.15) and energy price change (0.13) appear as the most important factors in the pairwise comparison of the factors based on hesitant fuzzy linguistic evaluations. The results coincide with the need for high efficiency in solar energy systems, the importance of governmental supportive policies and the effects of price competition in the energy sector. Also, the closeness of the overall priority values of all projects (0.189, 0.23, 0.287, 0.135, 0.158) indicates that the decision makers take into account the effective factors.

The Dominance Degree-Based Heterogeneous Linguistic Decision-Making Technique for Sustainable 3PRLP Selection

This study develops a novel dominance degree-based heterogeneous linguistic decision-making technique for identifying the most sustainable third-party reverse logistics providers (3PRLPs) under complex input environments. First, qualitative and uncertain inputs that arise from real-world 3PRLP evaluation process are successfully managed by using linguistic terms, hesitant fuzzy linguistic terms, and probabilistic linguistic term sets with different granularities. Then, the dominance degrees of each 3PRLP related to the other 3PRLPs are calculated based on a new ratio index-based probabilistic linguistic ranking method and the dominance matrix is constructed. Furthermore, to represent the closeness of each 3PRLP to the ideal solution, we propose a sort of measures including the dominance-based group utility measure, the dominance-based individual regret measure, and the dominance-based compromise measure. Accordingly, the selection results of 3PRLPs are obtained according to these measures. Finally, the developed method is applied to a case study from car manufacture industry, and the comparison analysis shows that the proposed method is reliable and stable for dealing with the problem of the 3PRLP selection. The main advantage of the developed method is that it cannot only well avoid the potential loss risks but also balance group utility scores and individual regret scores.

Hesitant Fuzzy Linguistic Consensus Model Based on Trust-Recommendation Mechanism for Hospital Expert Consultation

Aiming at validly improving the efficiency and quality of hospital expert consultation, a consensus model under uncertainty is established to operate a hospital decision support system (HDSS). Considering that expert consultation is commonly used in emergency events associated with life rescue where uncertainty and complexity exist, hesitant fuzzy linguistic terms are adopted. Additionally, since pairwise comparison information is efficient to express individual judgment, a novel concept of satisfaction degree toward hesitant fuzzy linguistic preference relation is utilized to measure the group consensus. A trust-recommendation mechanism is built to generate advice for experts. A consensus model is constructed to handle the HDSS in emergency decision-making problems. Some simulation experiments are designed to verify the availability and reasonability of the proposed model. Then, the proposed model is applied to address the selection of an emergency response plan for the influenza H7N9 virus, and finally, the strong robustness and powerful capability of the proposed model in the consensus reaching process is exhibited by making a comparative analysis among several recommendation mechanisms.

A New Decision Making Approach for Supplier Selection: Hesitant Fuzzy Axiomatic Design

This paper deals with the hesitancy in decision making. Since the decision makers generally doubt to evaluate the alternatives and the criteria in hesitant situations, the existing methods do not satisfy them. Therefore, hesitant versions of Fuzzy-AHP (HF-AHP) and Fuzzy Axiomatic Design (HF-AD) are introduced in this paper. HF-AHP lets the decision makers to use hesitant fuzzy linguistic terms while performing the pairwise comparisons when they are indecisive. HF-AD is used to define the system and design ranges of the items in a hesitant situation. In addition, a case study is revealed as a numerical example in which the best supplier is selected among six alternatives regarding five criteria. In that case study, both weighted and unweighted versions of HF-AD are used. In the weighted version, HF-AHP is used to determine the weights of the criteria. Furthermore, sensitivity analysis is performed to check the robustness of the decision. Moreover, these proposed techniques are compared with non-hesitant versions. According to the results, the decision makers feel more confident with the hesitant versions. Hence, the primary contributions of this study are to develop HF-AD and HF-AHP which are helpful for the decision makers to express their preferences in hesitant situations.

Hesitant Fuzzy Linguistic Aggregation Operators Based on the Hamacher t-norm and t-conorm

Hesitant fuzzy linguistic (HFL) term set, as a very flexible tool to represent the judgments of decision makers, has attracted the attention of many researchers. In recent years, some HFL aggregation operators have been developed to aggregate the HFL information. However, most of these operators are proposed based on the Algebraic product and Algebraic sum. In this paper, we presented some HFL aggregation operators to handle HFL information based on Hamacher triangle norms. We first define new operational laws on the HFL element according to Hamacher triangle norms. Then we present a family of HFL Hamacher aggregation operators, including the HFL Hamacher weighted averaging, HFL Hamacher weighted geometric, HFL Hamacher power weighted averaging and HFL Hamacher power weighted geometric operators and their generalized forms. We also investigate some special cases and properties of these operators in detail. Furthermore, we develop two approaches based on the proposed operators to deal with the multi-criteria decision-making problem with HFL information. Finally, a numerical example with regard to choosing a suitable city to release sharing car is provided to illustrate the feasibility of the proposed method, and the advantages of the proposed methods are shown by conducting a sensitivity and comparative analysis.

Distance measures for hesitant intuitionistic fuzzy linguistic term sets based on a risk factor parameter

ABSTRACTWe propose notions of some distance measures between two hesitant intuitionistic fuzzy linguistic term sets (HIFLTSs). Weighted distance measures between two collections of HIFLTSs are also proposed and analyzed for discrete and continuous cases. Based on the proposed distance measures, the relative closeness (RC) coefficients for different alternatives are established and then used to rank the alternatives in a multi-criteria group decision-making problem. Furthermore, a comparison between the RC coefficients of alternatives by using the proposed distance measures for different values of the risk factor parameter is given graphically. Two illustrative examples are provided to elaborate the applicability and advantage of the proposed approach for the selection of suitable alternative.

A Research on Multiple Criteria Group Decision-making of Suppliers Based on Hesitant Fuzzy Linguistic Terms

A Research on Multiple Criteria Group Decision-making of Suppliers Based on Hesitant Fuzzy Linguistic Terms