Linguistic Z-numbers Research Articles

A linguistic Z-number-based dual perspectives information volume calculation method for driving behavior risk evaluation

Driving risk evaluation is a critical task for traffic safety and vehicle insurance. Unfortunately, many existing methods disregard the unknown and uncertain nature of driving behavior in real-world scenarios. In this paper, we formula this task as a multi-criteria group decision-making problem and propose a dual perspective information volume method for evaluating the driving risk score based on linguistic Z-number. Our proposed method accurately captures the uncertainties and reliability of driving behavior by utilizing two fuzzy numbers A and B. The main contributions of our paper are as follows: firstly, we introduce the α-truncation set and the information volume-based utility function and credibility probability model, which form the basis of the vehicle evaluation method. Secondly, we consider cross-information to model the divergence of information between dual perspectives, based on different experts and features. We calculate the credibility value for each perspective using the Z+-based information volume. Thirdly, we present the dual perspective calculation method used to assess driving risk. This method assigns risk scores to individual vehicles based on a utility function. Finally, we conduct a case study using the historical trajectories of vehicles obtained from an insurance company. The results are compared against the damages coefficient. Furthermore, we perform dependability and parameter analysis to demonstrate the effectiveness and superiority of our approach. Our approach provides valuable insights and suggestions for the insurance industry and traffic safety departments in relevant fields.

Multi-stage type-α constrained minimum-cost consensus for linguistic Z-number large-scale decision-making

As an extension of group decision-making in terms of scale and uncertainty, linguistic Z-number large-scale decision-making (LZ-LSDM) is emerging as a prominent research topic in the field of decision science. The unique structure of LZ-LSDM presents new challenges for both clustering analysis and consensus building. Minimum-cost consensus (MCC) based on the optimization principle is widely recognized as an effective tool for managing the consensus-reaching process. However, there is a scarcity of literature that addresses the study of MCC within the context of LZ-LSDM, as well as the application of MCC in the identification and treatment of non-cooperative behaviors. To this end, this study proposes a punishment strategy-driven multi-stage type-α constrained MCC model for LZ-LSDM problems. First, a similarity constraint-based clustering method with linguistic Z-numbers is proposed. Given the clustering results, a type-α constrained MCC (α-CMCC) model with personalized feedback constraints is designed to provide a personalized solution for visualizing opinion adjustment and preventing over-adjustment. Based on the optimal solution obtained by α-CMCC, the identification rule for non-cooperative behaviors is proposed. We conclude three punishment strategies—namely, pure, mixed, and cross—to address non-cooperative behaviors by arranging and combining commonly used punishment approaches. Finally, we illustrate the feasibility and validity of the model through a case study designed to facilitate consensus among an online patient community on knowledge-based recommendations. An exhaustive comparative analysis reveals the advantages and features of the proposed consensus model.

A linguistic Z-number-based sorting method for three-way multi-criterion decision-making in ecological function zoning

Traditional multi-criterion decision-making methods can help decision-makers decide whether to accept or reject an alternative, but they cannot ensure these results fully meet decision-makers’ requirements. Three-way decision methods overcome this limitation by dividing alternatives into three regions. However, traditional three-way decision methods rarely considered the reliability of evaluation information and individual regrets under some criteria. Moreover, most weighting methods in three-way decisions ignored the influence of abnormal values. To fill the above gaps, this paper develops a linguistic Z-number-based sorting method combining the three-way decision with the gain and lost dominance score (GLDS) method that reflects individual regrets under some criteria. Firstly, to avoid individual regrets, we introduce the linguistic Z-number-based GLDS sorting (LZ-GLDS-Sort) method, which obtains two states of alternatives that will be used in the three-way decision. Linguistic Z-numbers can express linguistic evaluation and credibility. Two parameters are introduced in the LZ-GLDS-Sorting method to reflect the relative importance of evaluation information and credibility. Then, a three-way decision method is introduced to divide alternatives into three regions, in which the variation-coefficient weighting method is introduced to avoid the effect of abnormal values. Finally, the ecological function zoning as an example is provided to validate the applicability of this model.

Investment risk assessment of anaerobic fermentation based hydrogen production project based on regret linguistic Z-number averaging Choquet integral operator

Anaerobic fermentation based hydrogen production technology has a good market investment prospect for its advantages of abundant raw material sources, low cost and low energy consumption. The industry is in the early stage of development and will face many risks. The corresponding risk assessment has not yet attracted attention. Therefore, this paper constructs a risk assessment framework. Firstly, 17 critical risk factors affecting the anaerobic fermentation based hydrogen production project is identified and grouped into five categories. The linguistic Z-number is then used to describe the uncertainty. Later, a regret linguistic Z-number averaging Choquet integral operator is put forward to deal with interrelationships between criteria and simulate the decision-makers’ regret avoidance attitudes. In addition, a novel relative entropy measure is determined, wherein the expert weight model is established by combining the reliability measure. Subsequently, a case study is conducted and the result demonstrates the risk level of the project located in Hubei, China is between “Medium” and “Slightly high”. Finally, some measures are proposed to deal with these risks. This work can provide a theoretical basis for the risk prevention and investment decision of anaerobic fermentation based hydrogen production projects.

Failure mode and effect analysis using VIKOR method based on interval-valued linguistic Z-numbers

Failure mode and effect analysis (FMEA) is an effective quality management tool used to improve product quality and reliability. However, with the application of FMEA, its shortcomings are exposed regarding risk assessment, weight determination, and failure mode risk prioritization. This paper proposes a new FMEA model using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method based on the Interval-valued linguistic Z-numbers (IVLZNs). Specifically, IVLZNs and the Interval-valued linguistic Z-numbers weighted arithmetic averaging (IVLZNWAA) operator are used to evaluate and aggregate risk information of failure modes; the maximum deviation method is used to determine the weight of risk factors; the IVLZNs-VIKOR method is used to determine the risk priority of failure modes. Then, a numerical example is given to verify the effectiveness of the proposed model. Finally, a comparative analysis is made to demonstrate the feasibility and rationality of the proposed method.

Transparent Fault Diagnosis for Complex Equipment Considering Expert Reliability Based on Belief Rule Base and Linguistic Z-Number

Transparent Fault Diagnosis for Complex Equipment Considering Expert Reliability Based on Belief Rule Base and Linguistic Z-Number

Failure mode and effects analysis in consensus-based GDM for surface-guided deep inspiration breath-hold breast radiotherapy for breast cancer under the framework of linguistic Z-number

Most of the cancer patients cannot get the usual treatment in the advanced stage of the disease. In particular, for breast cancer, it is to use a special kind of radiation that targets the tumor while the patient holds their breath. This is called surface-guided (SG) breast radiotherapy (RT) with deep inspiration breath-hold (DIBH). This treatment is very complicated and different from the normal way of treating breast cancer. It can also have many errors and problems that are not well-studied or understood. To prevent these problems, we need to use a method called failure mode and effects analysis (FMEA). This method helps us find and rank the possible risks and how to avoid them. In keeping with this aim, this paper studies consensus-reaching process-based group decision-making (GDM) with completely unknown risk factors' weights and the gained-lost dominance score (GLDS)-based ranking process. We cannot avoid the uncertainty related to relativity information in medical science. Therefore, in this study, we offer the experts to provide their opinions using the linguistic Z number (LZN) because LZN is designed to capture such types of information in medical science. For the consensus-reaching process (CRP), the relativity among experts is considered for consensus measurement, and the minimum adjustment sum with group consensus opinion-based feedback is offered. For unknown weight of risk factors, we don't know how much each risk factor weighs. So, we use a method that combines two ways of calculating weights. One way is a subjective weight computation process, and the other way is based on the data using entropy and division. Finally, we applied our proposed CRP-based GDM approach for ranking the identified FMs related to the SG DIBH RT process. We conduct sensitive, comparative, and statistical assessments to ensure our method is reasonable and practical.

Group decision-making method based on expert credibility with multi-granularity probabilistic linguistic Z-number preference relation

Group decision-making method based on expert credibility with multi-granularity probabilistic linguistic Z-number preference relation

Preference-based regret three-way decision method on multiple decision information systems with linguistic Z-numbers

Three-way decision theory (3WD) provides a reasonable solution to solve multi-criteria decision-making (MCDM) problems and reduces more decision risks than two-way decision (2WD). However, the impact of preferences on decision results has not been reasonably considered in 3WD. Thus, this paper proposes a preference-based regret 3WD model on multiple decision information systems (MDISs) with linguistic Z-numbers (LZNs) to address the defect. First of all, a Z-LINMAP method is proposed by extending LINMAP method into the LZNs environment to derive criteria weights, Z-number ideal solutions and preference coefficients. On the basis of preference coefficients, the consistency-order and inconsistency-order are defined and further the consistency and inconsistency equivalence classes are presented to derive the conditional probabilities of alternatives. Then, the regret loss functions based on regret theory are presented and the regret 3WD rules of a single decision information system are designed. Furthermore, the weighted expected regret loss function and loss score function are defined to classify and rank all alternatives for MDISs. Finally, the proposed 3WD model is applied to the image recognition case with human-computer interaction to verify the effectiveness, and the comparative and sensitivity analyses are carried out to demonstrate the feasibility of our model.

An integrated method for multi-attribute group decision-making based on the linguistic Z-number and MSM operators

This article extends the ordered weighted average operator (OWA) in the linguistic Z-number (LZN) environment, increasing attention to the distribution of data itself, and this idea can also be combined with other operators. Specifically, for the weight of data, this paper gives consideration to both the preference of attributes and the distribution of data itself, gives the interval distribution induced OWA operator (IDIOWA), and combines the weight of attributes to obtain the LZN interval distribution induced hybrid weighted average operator (LZIDIHWA) in LZN environment. Then, it introduces some good properties of this operator. At the same time, the LZN interval distribution induced weighted Maclaurin symmetric means operator (LZIDIWMSM) is obtained by combining LZN interval distribution induced OWA operator (LZIDIOWA) with the LZN weighted Maclaurin symmetric means operator (LZWMSM), which makes up for the defect that LZWMSM cannot be used for data integration alone. Finally, the two operators are used for multi-attribute group decision-making (MAGDM), and their effectiveness is verified by comparative analysis.

A new approach to failure mode and effect analysis under linguistic Z-number: A case study of CNC tool holders

Failure Mode and Effects Analysis (FMEA), as the most important component of risk management, has been widely used in the engineering field. Although the method is widely used, the traditional FMEA approach does not take into account the issue of decision maker's mental preference, which seriously affects the accuracy of risk ranking results. To improve these shortcomings, we fuse the language set and Z-number to propose the language Z-number to express the decision information. Meanwhile, we propose a new linguistic Z-number weight Muirhead mean (LZNWMM) operator and use it to aggregate the information. Finally, we propose a new FMEA framework, which uses the proposed operator to aggregate information and calculate attribute weights using the fuzzy best-worst method. Using this method applied to a computer numerical control (CNC) tool holder manufacturer, its usability and validity are verified by comparing it with the traditional FMEA method, LZNWA method and LZNBM method.

A hybrid HFACS model using DEMATEL-ORESTE method with linguistic Z-number for risk analysis of human error factors in the healthcare system

The human error factor is one of the leading causes of medical errors. Among risk analysis techniques for human error factors, HFACS (Human Factor Analysis and Classification System) method has been regarded as one of the most valuable approaches due to its advantage in potential failure classification. However, the conventional HFACS method is insufficient to handle the risk analysis problem for human error factors considering the interactive relationship among these factors. Moreover, the current fuzzy HFACS frameworks cannot address quantitative risk analysis issues, including imprecision and reliability of information. Thus, this paper constructs an integrated linguistic Z-number-based HFACS framework for analyzing the risk of human error factors. This framework can capture the uncertainty and reliability of the risk evaluation information and the interactive relationships among factors. First, the linguistic Z-number-DEMATEL (Decision Making Trial and Evaluation Laboratory) method is used to generate the comprehensive risk matrix of human error factors identified by the HFACS method. Then, an extended linguistic Z-number-ORESTE method based on the score function is presented to prioritize the risk of human error factors, which can show the preference, indifference, and incomparability relationship among human error factors. Finally, a case study of a healthcare system is conducted to illustrate the reliability of the proposed method. The result of this case indicates that inadequate resources are the most severe risk. Sensitivity analysis and comparative analysis indicate the necessity of considering the effect of the semantics of language terms and the interrelationships between human error factors on risk analysis results. These results show that the proposed hybrid framework is a reliable means to analyze the risk of human error factors within the HFACS method.

Combination of linguistic Z-numbers and IF-THEN rules based on rectangular coordinate system

There is a growing body of literature that recognises the importance of Z-numbers proposed by Prof Zadeh. However, due to the complicated structure and short presentation time, many unknowns about Z-numbers still exist. To fill these gaps, this study aims to make use of rectangular coordinate system to express linguistic Z-numbers. Simultaneously, this study sets out to design a score function to quantify the information contained in different Z-numbers. Subsequently, distance measure and similarity measure are also presented from the perspective of coordinate system. Besides, linguistic discrete Z-numbers and belief rule base (BRB) model are combined to construct a novel reasoning model on the basis of implication operators. To verify the validity of the proposed method, three representative examples of epidemic level assessment, multicriteria group decision-making (MCGDM), and network security assessment are employed. The comparison with other widely used methods are performed to further demonstrate the superiority of the proposed method.

Novel Aczel-Alsina operations-based linguistic Z-number aggregation operators and their applications in multi-attribute group decision-making process

The change from multi-attribute decision-making (MADM) to multi-attribute group decision-making (MAGDM) has led to a doubling of data volume and a trend toward multi-attribute large group decision-making. At this time, operators need to be used to integrate these data, while also reflecting the psychological impact of decision-makers. Linguistic Z-number (LZN) is widely used for evaluating fuzzy information due to its unique flexibility and accuracy. However, in the LZN environment, aggregation operators, as an important component of decision-making, also have some shortcomings. Some aggregation operators currently used in LZN environments have low flexibility, high computational complexity, and are not convenient. This article cleverly utilizes Aczel Alsina t-norm and t-norm to obtain some aggregation operators with good algebraic properties, and proves these properties, which can also reflect the psychological influence of decision-makers. On this basis, we obtained a MAGDM model and successfully applied it in the following example, comparing it with other operators and verifying its superiority.

A hybrid generalized TODIM based risk prioritization method for failure mode and effect analysis with linguistic Z-numbers

The traditional failure mode and effect analysis (FMEA), as an effective risk analysis technique, has several limitations in the uncertainty modeling and the weights determination of the risk indicators. This paper aims to propose a hybrid risk prioritization method simultaneously considering the characteristics of the reliability associated with the FMEA team members’ evaluation information and their psychological behavior to enhance the performance of the traditional FMEA model. The hybrid risk prioritization method is developed based on the generalized TODIM method and the weighted entropy measure with the linguistic Z-numbers (LZNs). First, the LZNs are adopted to depict the FMEA team members’ cognition information and the reliability of these information. Second, a weighted entropy measure based on the fuzzy entropy and the LZNs is developed to obtain the risk indicators’ weights. Finally, the generalized TODIM method with the LZNs is constructed to obtain the risk priority orders of failure modes, which can effectively simulate the FMEA team members’ psychological character. The applicability and effectiveness of the proposed risk prioritization method is validated through an illustrative example of an integrated steel plant. The results of sensitivity analysis and comparative analysis indicate that the proposed hybrid risk prioritization method is effective and valid, and can get more accurate and practical risk ranking results to help enterprises formulate accurate risk prevention and control plans.

A flexible group decision-making method for green supplier selection integrating MABAC and CRITIC method under the linguistic Z-numbers environment

This paper intends to treat the green supplier selection (GSS) problem as a multi-attribute group decision making (MAGDM) problem, adopt the linguistic Z-number that can more flexibly and accurately express the evaluation information, and expand the traditional multi-attribute boundary approximate area comparison (MABAC) method, combine the CRITIC method of standard importance and consider the risk vector to finally determine the optimal solution. More specifically, the linguistic Z-number is used to describe the fuzzy evaluation information of experts on alternatives under attributes, then the expanded CRITIC method is used to obtain the weight of each given attribute, and finally the MABAC method with added risk vector and expanded is used to obtain the ranking of alternatives and obtain the best solution. Finally, taking green supplier selection as an example, and comparing with other methods, the reliability and effectiveness of the constructed method are verified. The results show that this method can express the evaluation information of experts flexibly and completely, and obtain the ranking results of given schemes through fewer steps, which is reliable and effective.

Evaluating Internet hospitals by a linguistic Z-number-based gained and lost dominance score method considering different risk preferences of experts

Internet hospitals are platforms that provide medical information, prescription, and treatment by combining online and offline medical resources. Well-development Internet hospitals can reduce operational pressure of offline hospitals and unnecessary waste of medical resources. Evaluating the performance of Internet hospitals is helpful for their development. In this paper, we introduce a linguistic Z-number-based gained and lost dominance score (GLDS) method to evaluate Internet hospitals. Firstly, to ensure the reliability of evaluation information, we extend the GLDS method to the linguistic Z-number environment. To model the risk preference of an expert, we depict the negative performance of an alternative over other alternatives under each criterion by a lost dominance score function, and represent the individual regret of a expert to the worst alternative by a net lost dominance score function. A weighting method that combines the best worst method and correlation coefficient method is developed to determine the weights of criteria. To verify the validity and practicability of the proposed method, a case study of ranking Internet hospitals is provided. Comparative analysis shows that the solutions of the proposed method reflect the preferences of experts.

Risk Evaluation of Logistics Park Projects’ Lifecycle during the COVID-19 Pandemic: Failure Mode and Effects Analysis

Logistics parks served as critical logistics facilities during the coronavirus disease 2019 pandemic. Constructing these large-scale projects including their subsequent operations involved unusual risk factors that required comprehensive risk evaluations. Therefore, it is vital to examine the potential risk factors across the entire life cycle of these projects in accordance with the complexity of these large-scale logistics park projects (LLPPs) during the pandemic. This study proposes a new risk analysis model that evaluates a comprehensive list of risk factors based on the failure mode and effects analysis (FMEA). The hesitant uncertain linguistic Z-numbers (HULZNs) and fuzzy C-means (FCM) clustering algorithm are incorporated into the FMEA model to evaluate the risk. The practicability of the model was assessed in a case study. The findings identified and distinguished risk factors based on their levels of influences and priorities in dealing with uncertainties during the decision-making process in LLPPs. The FMEA model’s new methodological assessment also contributes to the efficient risk evaluation process through its more reliable and reasonable failure mode risk clustering, particularly for complex group decision-making environments during the pandemic.

Clustering Z-information based on a system of fuzzy reference requirements

The paper develops a clustering model of multi-criteria object evaluations, taking into account the reliability of the results obtained. Clustering is based on a system of fuzzy reference requirements about the importance of the evaluated characteristics of objects for each of the clusters. Object evaluations are formalized on the basis of linguistic Z-numbers, both fuzzy numbers of which are the values of linguistic variables. Information for each object and each cluster is presented as a set of pairs (according to the number of characteristics) consisting of a fuzzy number (importance of a characteristic for the corresponding cluster) and a Z-number (an evaluation of the object within this characteristic and its reliability). Using this information, fuzzy ratings are determined for each object in accordance with fuzzy reference requirements for each cluster. Fuzzy ratings of objects, defined as fuzzy numbers, reflect the compliance of multi-criteria ratings of objects with fuzzy reference requirements. The comparative analysis of fuzzy ratings of all objects within one cluster proposed in the paper makes it possible to identify the best representative (or best representatives) of the cluster under consideration and determine the degree of belonging of the remaining objects to this cluster. The analysis is carried out for all clusters. A numerical example is given, which shows the effectiveness of the developed method under Z-information.

Developing sustainable competitive advantages from the lens of resource-based view: evidence from IT sector of an emerging economy

ABSTRACT The present study examines the IT industry’s sustainable competitive advantages (CAs) from a resource-based view (RBV). Therefore, a list of CAs in three areas of the triple bottom line (TBL), including economic, social, and environmental, are extracted by thoroughly studying the relevant literature. These attributes are then evaluated by small and medium enterprises (SMEs) managers in the IT sector by implementing a Multi-level hybrid Decision-Making approach. This step employs a modified Delphi technique combined with linguistic z-numbers. Further, by reviewing the literature, the list of intangible resources is counted, and the importance of these resources is measured via the linguistic z-number BWM (Best Worst Method). Finally, the degree of the controllability of critical CAs is prioritised by z-number TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and then classified found on the resources. Consequently, different classes are discussed, and appropriate strategies for dealing with each are presented.