Uncertain Linguistic Variables Research Articles

Group decision making method for third-party logistics management: An interval rough cloud optimization model

Group decision making in third-party logistics service selection plays an essential role for improving service quality, increasing efficiency and reducing the net cost. Fuzzy and uncertain linguistic variables are commonly used to represent experts‘rankings in optimization problems. To recognize the limits of human cognition and subjectivity of human evaluations, several optimization approaches have been studied to select remanufacturing alternatives in decision making processes, however these methods have certain deficiencies such as lacking manipulation tools of diverse information, randomness, use of predefined parameters increasing uncertainty, interpersonal relations among evaluation criteria. The integration of interval numbers, rough approximations, and cloud model theory plays a significant role to model incomplete and inadequate information occurring in decision making problems. This research paper focuses on the integration of dual interval rough integrated cloud model with best-worst optimization technique, Multi-Attributive Border Approximation area Comparison (MABAC) and Weighted Aggregated Sum Product Assessment (WASPAS) approaches. A novel min–max optimization model based dual interval rough integrated cloud values is designed to compute the weight coefficients and consistency ratio for each criteria. The consistency of proposed optimization model is checked using a consistency ratio test. Secondly, the alternatives are ranked using the proposed DIRI cloud based MABAC and WASPAS approaches using interval clouds based weighted sum and weighted product coefficients, approximation area values and distance formulae. The significance of the proposed model is highlighted with a case study of third-party logistics service management of an electronic firm. The rationality and out-performance of the proposed methodology is studied by a comparative analysis with existing approaches and detailed sensitivity analysis on different variations of criteria weights and parameter.

Enhancing System Safety and Reliability through Integrated FMEA and Game Theory: A Multi-Factor Approach

This study aims to address the limitations of traditional Failure Mode and Effect Analysis (FMEA) in managing safety and reliability within complex systems characterized by interdependent critical factors. We propose an integrated framework that combines FMEA with the strategic decision-making principles of Game Theory, thereby enhancing the assessment and mitigation of risks in intricate environments. The novel inclusion of the Best Worst Method (BWM) and Pythagorean fuzzy uncertain linguistic variables refines the accuracy of risk evaluation by overcoming the inherent deficiencies of conventional FMEA approaches. Through sensitivity analysis, the framework’s efficacy in identifying and prioritizing failure modes is empirically validated, guiding the development of targeted interventions. The practical application of our methodology is demonstrated in a comprehensive healthcare system analysis, showcasing its versatility and significant potential to improve operational safety and reliability across various sectors. This research is particularly beneficial for systems engineers, risk managers, and decision-makers seeking to fortify complex systems against failures and their effects.

Multi-Dimensional Cloud Model-Based Assessment and Its Application to the Risk of Supply Chain Financial Companies

The multi-dimensional cloud model is proposed as the expansion of the one-dimensional cloud model. The features of ambiguity and stochasticity in complex information situations are considered; thus, this optimized model can be utilized upon multiple value classifications and ordering via which the objects' attributes of physical and social can be reflected. Therefore, this promoted model is wildly used. This paper provides a knowledge graph by reviewing the theoretical research of the multi-dimensional cloud model and its related bibliographies, and Cite Space is applied here to give a visualization conclusion. In recent years, a multitude of theories and methods have emerged to address the challenges posed by fuzzy and stochastic uncertainty in various domains, such as image segmentation, data mining, prediction techniques, and comprehensive evaluation of multiple metrics and dimensions using uncertain linguistic variables.

Data governance services evaluation: a multi-attribute group decision-making method with multigranular uncertain linguistic variables

PurposeGovernment organizations often store large amounts of data and need to choose effective data governance service to achieve digital government. This paper aims to propose a novel multi-attribute group decision-making (MAGDM) method with multigranular uncertain linguistic variables for the selection of data governance service provider.Design/methodology/approachThis paper presents a MAGDM method based on multigranular uncertain linguistic variables and minimum adjustment consensus. First, a novel transformation function is proposed to unify the multigranular uncertain linguistic variables. Then, the weights of the criteria are determined by building a linear programming model with positive and negative ideal solutions. To obtain the consensus opinion, a minimum adjustment consensus model with multigranular uncertain linguistic variables is established. Furthermore, the consensus opinion is aggregated to obtain the best data governance service provider. Finally, the proposed method is demonstrated by the application of the selection of data governance service provider.FindingsThe proposed consensus model with minimum adjustments could facilitate the consensus building and obtain a higher group consensus, while traditional consensus methods often need multiple rounds of modifications. Due to different backgrounds and professional fields, decision-makers (DMs) often provide multigranular uncertain linguistic variables. The proposed transformation function based on the positive ideal solution could help DMs understand each other and facilitate the interactions among DMs.Originality/valueThe minimum adjustment consensus-based MAGDM method with multigranular uncertain linguistic variables is proposed to achieve the group consensus. The application of the proposed method in the selection of data governance service provider is also investigated.

Gray Uncertain Linguistic Multiattribute Group Decision Making Method Based on GCC-HCD

This article presents a multiattribute group decision-making (MAGDM) method using cloud models to handle decision problems with gray uncertain linguistic variables that contain both subjective and objective uncertainty. In this new method, the new concepts of gray uncertain linguistic variable and gray comprehensive cloud are defined, and a method for converting gray uncertain linguistic variables into gray comprehensive clouds is provided. A gray comprehensive clouds-weighted averaging (GCC-WA) operator is proposed to aggregate the information with multiple gray comprehensive clouds. A distance measure between two gray comprehensive clouds is defined to form the gray comprehensive cloud-hybrid closeness degree (GCC-HCD), which is then applied to rank the alternatives. An example of an offshore mining investment is applied to validate the method. Sensitivity analysis and comparison analysis are carried out with other congeneric methods.

T-spherical uncertain linguistic MARCOS method based on generalized distance and Heronian mean for multi-attribute group decision-making with unknown weight information

The T-spherical uncertain linguistic (TSUL) sets (TSULSs) integrated by T-spherical fuzzy sets and uncertain linguistic variables are introduced in this article. This new concept is not only a generalized form but also can integrate decision-makers’ quantitative evaluation ideas and qualitative evaluation information. The TSULSs serve as a reliable and comprehensive tool for describing complex and uncertain decision information. This paper focuses on an extended MARCOS (Measurement of Alternatives and Ranking according to the Compromise Solution) method to handle the TSUL multi-attribute group decision-making problems where the weight information is completely unknown. First, we define, respectively, the operation rules and generalized distance measure of T-spherical uncertain linguistic numbers (TSULNs). Then, we develop two kinds of aggregation operators of TSULNs, one kind of operator with independent attributes is T-spherical uncertain linguistic weighted averaging and geometric (TSULWA and TSULWG) operators, and the other is T-spherical uncertain linguistic Heronian mean aggregation operators (TSULHM and TSULWHM) considering attributes interrelationship. Their related properties are discussed and a series of reduced forms are presented. Subsequently, a new TSUL-MARCOS-based multi-attribute group decision-making model combining the proposed aggregation operators and generalized distance is constructed. Finally, a real case of investment decision for a community group-buying platform is presented for illustration. We further test the rationality and superiorities of the proposed method through sensitivity analysis and comparative study.

Workload balance-based dynamic two-sided matching decision-making approach for cloud manufacturing tasks and services under uncertain preferences

PurposeThis paper aims to develop a dynamic two-sided stable matching method based on preference information of the matching objects in uncertain environments, so as to solve the matching problem of cloud manufacturing tasks and services with load balancing.Design/methodology/approachFor dynamic two-sided matching, due to the complexity of social environment and the limitation of human cognition, hesitation and fuzziness always exist in the process of multi-criteria assessment. First, in order to obtain the accurate preference information of each matching object, uncertain linguistic variables, uncertain preference ordinal and incomplete complementary matrices are used to evaluate multi-criteria preference information. This process is undertaken by considering the probability of each possible matching pair. Second, the preference information at different times is integrated by using the time-series weight to obtain the comprehensive satisfaction degree matrices of the matching objects. Further, the load adjustment parameter is used to increase the satisfaction degree of the matching objects. Afterward, a dynamic two-sided stable matching optimization model is constructed by considering stable matching conditions. The model aims to maximize the satisfaction degree and minimizes the difference in the satisfaction degree of matching objects. The optimal stable matching results can be obtained by solving the optimization model. Finally, a numerical example and comparative analysis are presented to demonstrate the characteristics of the proposed method.FindingsUncertain linguistic variables, uncertain preference orders and incomplete complementary matrices are used to describe multi-criteria preference information of the matching objects in uncertain environments. A dynamic two-sided stable matching method is proposed, based on which a DTSMDM (dynamic two-sided matching decision-making) model of cloud manufacturing with load balancing can be constructed. The study proved that the authors can use the proposed method to obtain stable matching pairs and higher matching objective value through comparative analysis and the sensitivity analysis.Originality/valueA new method for the two-sided matching decision-making problem of cloud manufacturing with load balancing is proposed in this paper, which allows the matching objects to elicit language evaluation under uncertain environment more flexibly to implement dynamic two-sided matching based on preference information at different times. This method is suitable for dealing with a variety of TSMDM (two-sided matching decision-making) problems.

A new three-way group decision-making model based on geometric heronian mean operators with q-rung orthopair uncertain linguistic information

As an effective tool for three-way decisions (3WD) problems, decision-theoretic rough sets (DTRSs) have raised increasing attention recently. In view of the advantages of q-rung orthopair uncertain linguistic variables (q-ROULVs) in depicting uncertain information, a new DTRSs model based on q-ROULVs is proposed to solve three-way group decision-making (3WGDM) problems. Firstly, the loss function of DTRSs is depicted by q-ROULVs and a q-rung orthopair uncertain linguistic DTRSs model is constructed subsequently. Secondly, to aggregate different experts’ evaluation results on loss function in group decision-making (GDM) scenario, the q-rung orthopair uncertain linguistic geometric Heronian mean (q-ROULGHM) operator and the q-rung orthopair uncertain linguistic weighted geometric Heronian mean (q-ROULWGHM) operator are presented. Related properties of the proposed operators are investigated. Thirdly, to compare the expected loss of each alternative, a new score function of q-ROULVs is defined and the corresponding decision rules for 3WGDM are deduced. Finally, an illustrative example of venture capital in high-tech projects is provided to verify the rationality and effectiveness of our method. The influence of different conditional probabilities and parameter values on decision results is comprehensively discussed.

Multi-attribute group decision making method with dual comprehensive clouds under information environment of dual uncertain Z-numbers

Recognizing the subjectivity in human judgment and the limits to human cognition, fuzzy linguistic variables and uncertain linguistic variables are widely used to express the preference information of the decision experts in Multi-Attribute Group Decision Making (MAGDM). This paper develops an MAGDM method to solve a decision problem in which the attribute values are fuzzy numbers characterized by uncertain linguistic variables, that is, dual uncertain Z-numbers. The notion of the dual uncertain Z-number is first proposed based on the classical Z-number. Next, the cloud model is introduced, together with a new notion of the dual comprehensive cloud. A method of transforming the dual uncertain Z-numbers into dual comprehensive clouds is proposed. Third, a new dual comprehensive cloud-weighted averaging operator (DCC-WA) is presented to aggregate the multiple dual comprehensive clouds. Fourth, a new dual comprehensive cloud-grey relational degree (DCC-GRD) is supplied, and a novel MAGDM method based on the DCC-GRD is proposed. Finally, a group decision making case of sustainable supplier selection is provided to validate the proposed method. A sensitivity analysis and comparison with several congeneric methods are conducted.

PREFERENCE AGGREGATION MODEL CONSTRUCTION OF GROUP MANAGEMENT DECISION-MAKING BASED ON RELATIVE ENTROPY AND NORM OPERATOR

Due to the complexity of the problem in the actual group management decision, the ambiguity of the information of all parties and the uncertainty of the decision environment, it is often difficult for decision-makers to describe the decision information by quantitative methods, but to give uncertain linguistic variables. Based on the analysis of previous research results, this paper proposes a group management decision-making aggregation model based on relative entropy and norm operator. First, the model uses the operation of [Formula: see text]-norm and new [Formula: see text]-norm operator to make the fuzzy set satisfy the ranking rate and contradiction rate. Then, based on the reliability of relative entropy, the attribute weights of different expert decisions are obtained, and the optimal assembly model of the corresponding group decision is established, respectively. The optimal solution and decision algorithm are given for each optimization model. The last example of the algorithm proves the validity and feasibility of the model, and can effectively avoid the distortion and distortion in the process of group management decision preference aggregation.

Heronian Mean Operators Based on Novel Complex Linear Diophantine Uncertain Linguistic Variables and Their Applications in Multi-Attribute Decision Making

In this manuscript, we combine the notion of linear Diophantine fuzzy set (LDFS), uncertain linguistic set (ULS), and complex fuzzy set (CFS) to elaborate the notion of complex linear Diophantine uncertain linguistic set (CLDULS). CLDULS refers to truth, falsity, reference parameters, and their uncertain linguistic terms to handle problematic and challenging data in factual life impasses. By using the elaborated CLDULSs, some operational laws are also settled. Furthermore, by using the power Einstein (PE) aggregation operators based on CLDULS: the complex linear Diophantine uncertain linguistic PE averaging (CLDULPEA), complex linear Diophantine uncertain linguistic PE weighted averaging (CLDULPEWA), complex linear Diophantine uncertain linguistic PE Geometric (CLDULPEG), and complex linear Diophantine uncertain linguistic PE weighted geometric (CLDULPEWG) operators, and their useful results are elaborated with the help of some remarkable cases. Additionally, by utilizing the expounded works dependent on CLDULS, I propose a multi-attribute decision-making (MADM) issue. To decide the quality of the expounded works, some mathematical models are outlined. Finally, the incomparability and relative examination of the expounded approaches with the assistance of graphical articulations are evolved.

Evaluation of autonomous vehicle driving systems for risk assessment based on three-dimensional uncertain linguistic variables

Autonomous vehicle driving systems (AVDSs) have independent decision structures from the users to manage and control the operations of the vehicles both in normal conditions and unexpected situations. Although there are some advantages, such as decreasing accidents reasoned by human errors and efficient energy usage, it is obvious that some risks are rooted in this technology usage. Therefore, it will be beneficial to realize a risk assessment application for autonomous vehicles (AVs) and/or driving systems (DSs) since whose risks are crucial to test and solve. In this paper, a multi-criteria decision making (MCDM) methodology integrating DEcision MAking Trial and Evaluation Laboratory (DEMATEL), Analytical Network Process (ANP), and VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) techniques under spherical fuzzy environment have been suggested to evaluate AVDS alternatives in terms of considered risk criteria. Spherical fuzzy sets (SFS), which are the extension of the ordinary fuzzy sets, have been used to consider the hesitancy of experts and decision-makers as well as uncertainty and impreciseness in the available data. In the application, six AVDS alternatives have been evaluated in terms of seven main criteria and forty sub-criteria. The factors “Software Specifications” and “Reliability” have been determined as the most important main and sub-criteria with the weights 0.193 and 0.066, respectively. Additionally, comparative and sensitivity analyses have been applied to present flexibility, validation and verification of the proposed methodology together with the sensitivity of the given decisions. Based on the application results and conducted analyses, possible implications by views of theoretical, managerial, and policy context have been discussed.

A method of multiple-attribute group decision making problem for 2-dimension uncertain linguistic variables based on cloud model

A method of multiple-attribute group decision making problem for 2-dimension uncertain linguistic variables based on cloud model

Multicriteria Decision Making With Incomplete Weights Based on 2-D Uncertain Linguistic Choquet Integral Operators.

In regard to multicriteria decision making (MCDM) problems where the values of the criteria are expressed by 2-D uncertain linguistic variables (2DULVs), where the criteria are interactive and the criteria weights are incompletely known, two novel MCDM methods are proposed in this paper. First, we offer some novel operational laws of 2DULVs, which can avoid the operational results exceeding the boundary of linguistic term sets. Then, we propose four operators to capture the interactions over the criteria, namely, the 2-D uncertain linguistic Choquet averaging (2DULCA) operator, the 2-D uncertain linguistic Choquet geometric (2DULCG) operator, the Shapley 2DULCA (S2DULCA) operator, and the Shapley 2DULCG (S2DULCG) operator. In addition, we establish the models based on the maximization deviation approach and the Shapley function to get the criteria weights. Finally, we propose two novel MCDM methods under 2-D uncertain linguistic environments, where four examples are used to explain the created MCDM methods. Comparative experimental results are presented to highlight the superiorities of the created approaches.

A multi-aspect coordination HDRED site selection framework under multi-type heterogeneous environments

Site selection is a critical link in hot dry rock exploration and development (HDRED). However, some problems such as incoordination of the qualitative and quantitative criteria, inaccurate information descriptions, information loss or distortion, incomplete consideration of the coordinating influence of the subjective-objective aspects, and criteria’s complex interactions exist in the extant site selection methods. To overcome those deficiencies, an innovative multi-aspect coordination framework under heterogeneous environments is proposed to select the optimal HDRED site. First, a comprehensive HDRED site selection criteria system is established from the perspective of sustainability. Second, multi-type heterogeneous information of crisp numbers, interval numbers, triangular intuitionistic fuzzy numbers, and interval-valued intuitionistic uncertain linguistic variables is adopted to accurately describe criteria, which not only can achieve varied criteria coordination, but improves the collection and processing of criteria information. Third, the heterogeneous entropy-DEMATEL model is proposed to more reasonably determine weights. Next, the heterogeneous TOPSIS is constructed to rank alternatives while avoiding information distortion or loss. Finally, a case from China is considered for comparative analysis and sensitivity analysis. The results indicate that the proposed framework can effectively promote the large-scale development of hot dry rock and has strong practical and popularizing value.

Multi-attribute group decision making method under 2-dimension uncertain linguistic variables

A method is proposed to deal with the uncertain multiple attribute group decision making problems, where 2-dimension uncertain linguistic variables (2DULVs) are used as the reliable way for the experts to express their fuzzy subjective evaluation information. Firstly, in order to measure the 2DULVs more accurately, a new method is proposed to compare two 2DULVs, called a score function, while a new function is defined to measure the distance between two 2DULVs. Secondly, two optimization models are established to determine the weight of experts and attributes based on the new distance formula and a weighted average operator is used to determine the comprehensive evaluation value of each alternative. Then, a score function is used to determine the ranking of the alternatives. Finally, the effectiveness of the proposed method is proved by an illustrated example.

Aggregation operators and VIKOR method based on complex q-rung orthopair uncertain linguistic informations and their applications in multi-attribute decision making

Complex q-rung orthopair uncertain linguistic set (CQROULS) is a combination of complex q-rung orthopair fuzzy set (CQROFS) and uncertain linguistic variable set (ULVS) as a proficient technique to express uncertain and awkward information in real decision theory. CQROULS contains uncertain linguistic variables, truth and falsity grades, which gives extensive freedom to decision makers for taking a decision as compared to CQROFS and their special cases. In this article, a new concept of fuzzy set, called CQROULS using CQROFS and ULVS is explored, and this can examine the qualitative assessment of decision makers and gives them extensive freedom in reflecting their belief about allowable truth grades. Based on the established operational laws and comparison methods for CQROULSs, the notions of complex q-rung orthopair uncertain linguistic weighted-averaging aggregation operator and complex q-rung orthopair uncertain linguistic weighted geometric aggregation operator are explored. Some special cases and the desirable properties of the explored operators are also established and studied. Additionally, the notion of VIseKriterijumska Optimizacija I KOmpromisno Resenje (VIKOR) method based on CQROULSs is explored, with the help of a numerical example, it is verified and also its comparative study is established. Moreover, based on the above analysis, we establish a method to solve the multi-attribute group decision making problems, in which the evaluation information is shown as CQROULNs. Finally, we solve some numerical examples using some decision making steps and explain the verity and proficiency of the explored operators by comparing with other methods, the advantages and graphical interpretation of the explored work are also discussed.

Dual generalized Bonferroni mean operators based on 2-dimensional uncertain linguistic information and their applications in multi-attribute decision making

The dual generalized Bonferroni mean operator is a further extension of the generalized Bonferroni mean operator which can take the interrelationship of different numbers of attributes into account by changing the embedded parameter. The 2-dimensional uncertain linguistic variable (2DULV) adds a second dimensional uncertain linguistic variable (ULV) to express the reliability of the assessment information in first dimensional information, which is more rational and accurate than the ULV. In this paper, for combining the advantages of them, we propose the dual generalized weighted Bonferroni mean operator for 2DULVs (2DULDGWBM) and the dual generalized weighted Bonferroni geometric mean operator for 2DULVs (2DULDGWBGM). In addition, we explore several particular cases and some rational characters of them. Further, a new approach is introduced to handle multi-attribute decision making problems in the environment of 2DULVs by the proposed operators. Finally, we utilize several illustrative examples to testify the validity and superiority of this new method by comparing with several other methods.

A New Linguistic Petri Net for Complex Knowledge Representation and Reasoning

Fuzzy Petri nets (FPNs) are a useful instrument for modelling expert systems to conduct knowledge representation and reasoning. Many studies have been carried out for improving the performance of FPNs in terms of their accurate representation of knowledge and power of approximate reasoning. Nevertheless, the current representation methods with FPNs are unable to handle the uncertain linguistic knowledge given by domain experts and the reliability of their judgments. In addition, the existing reasoning algorithms have no way to capture the interrelationship of the propositions with the same output transition. Therefore, we present a new type of FPNs, called 2-dimensional uncertain linguistic Petri nets (2DULPNs). The 2-dimensional uncertain linguistic variables (2DULVs) and Choquet integral are combined for knowledge representation and reasoning for the first time. The truth degrees of propositions, thresholds and certainty values of linguistic production rules are denoted as 2DULVs. Some new aggregated operators based on Choquet integral are proposed and used in the approximate reasoning to capture the interactions among antecedent propositions. Finally, an equipment fault diagnosis example is provided to illustrate the correctness and effectiveness of the proposed 2DULPN model.

Cloud model-based PROMETHEE method under 2D uncertain linguistic environment

This paper proposes a cloud model-based Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) method with 2D uncertain linguistic variables (2DULVs). 2DULVs are adopted by decision makers (DMs) to evaluate each alternative under the criteria because they can provide extra eva luation information. Cloud model is adopted to depict randomness and fuzziness. The possibility degree and possibility degree index are defined to develop an improved PROMETHEE II method for sorting alternatives. Entropy weight method is used to calculate the weight of each criterion. A renewable energy performance sample is used to illustrate the applicability of the proposed method. Sensitivity analysis and four comparative experiments demonstrate the stability and accuracy of the proposed approach.