Multi-attribute Group Decision Research Articles

Fermatean trapezoidal fuzziness average aggregation scheme for selection of infant clothing by group decision-making

The textile industry has gradually increased its use of various chemicals and the quantity of residues produced. Newborn baby apparel should be made of safe materials since it often comes into touch with them. Infant cloth selection is determined via group decision-making in Fermatean trapezoidal fuzzy number (FTFN). This study provides a new score and accuracy function and weighted, ordered, and hybrid averaging aggregation operators on FTFN. The properties of the proposed averaging aggregation operators on FTFN are presented. An algorithm for the multi-attribute group decision-making (MAGDM) approach will be proposed by applying the proposed averaging aggregation operators. The case studies concentrate on choosing newborn clothing with reduced chemical content within the textile sector. The presentation of the sensitivity analysis of the criterion weights is conducted to guarantee the durability and stability of the framework that has been implemented. Furthermore, a comprehensive analysis compares the novel framework with previous models, highlighting its superior performance.

Minimum adjustment cost consensus model with bounded risk preference attitudes: A perspective based on fractional stochastic dominance

Linguistic multi-attribute group decision making with linguistic distribution assessment (LMAGDM-LDA) problems have been widely investigated. However, in the existing studies, the risk attitudes of decision maker are seldom considered. Inspired by this, in this paper, we employ the idea of fractional stochastic dominance to develop a consensus reaching framework for LMAGDM-LDA with bounded risk preference attitudes. Then, we design an interactive learning algorithm for obtaining the bounded risk preference threshold. Next, we analyze the deception behaviors and average behaviors of experts, and develop the minimum adjustment cost consensus model with bounded risk preference attitude (MACC-BRP). And we justify the connection between the MACC-BRP and the minimum adjustment cost consensus model with other risk attitudes. Furthermore, we provide a distance-based method to obtain the rankings of alternatives. Furthermore, a numerical analysis is provided to show the computation process of the proposed method, and a comparison analysis and a simulation analysis are further conducted to show the advantages and features of the proposal. The simulation results show the effects of the risk preference threshold, the intrinsic individual weight and the average threshold. The comparison results show that the proposed minimum adjustment cost consensus models have clear advantages.

A Relative Projection-Based Multiattribute Group Decision-Making Model With Noncooperative Behavior Management and Application to NEV Supplier Selection.

This article reports a novel consensus model where a group of internal and external experts evaluate alternatives under multiple attributes and provide mutual evaluations. First, different from previous studies, the cognitive and interest conflicts of internal and external experts are considered simultaneously. But interest conflict is emphasized for internal experts, and cognitive conflict is mainly considered for external experts. Second, we explore the categorization and management methods of noncooperative behaviors (NCBs) of experts. The relative projection-based indexes are proposed for the first time to measure the degrees of cognitive and interest conflicts by using multiattribute preference matrices (MAPMs) and the weight vectors of attributes. A group of experts are divided into three categories and the corresponding management strategies are developed. Third, we investigate the consensus mechanism among experts with cognitive and interest conflicts. For reaching an acceptable consensus level, an adjustment process is proposed to revise some local entries in MAPMs and mutual evaluation matrix (MEM). A penalty mechanism is further established to dynamically update the weights of experts. An algorithm is designed to capture the consensus reaching process in multiattribute group decision making, where internal and external experts are distinguished by proposing a parameter. Finally, the high-performance battery supplier selection of new energy vehicle is studied to illustrate the proposed model. The results reveal that the efficiency of reaching consensus can be enhanced by using the developed model with effective management of NCBs.

Enhancing Renewable Energy Evaluation: Utilizing Complex Picture Fuzzy Frank Aggregation Operators in Multi-Attribute Group Decision-Making

Renewable energy resources are pivotal in addressing global energy challenges and achieving sustainable development goals. In this context, the complex picture fuzzy sets (CPFS) theory provides a robust framework for handling complex and uncertain information. This article presents innovative strategies based on Frank aggregation tools to evaluate and prioritize renewable energy resources. By incorporating the concepts of abstinence value, membership value, and non-membership values in the CPFS framework, the characteristics and capabilities of the CPFS are extended to represent a broader range of information. Specifically, we introduce novel operators such as the CPF Frank weighed average (CPFFWA) and CPF Frank weighed geometric (CPFFWG) operators, effectively handling insufficient and unpredictable information during the aggregation process. Moreover, we explore specialized variants of these operators, such as the CPF Frank order weighed average (CPFFOWA) and CPF Frank order weighed geometric (CPFFOWG) operators, which possess distinct characteristics suitable for specific decision-making scenarios. The proposed methodologies are applied within the framework of multi-attribute group decision-making (MAGDM) to evaluate and identify optimal renewable energy options from a group of alternatives. Through comparative analyses and validation against existing approaches, the advantages and consistency of our developed operators are demonstrated. The findings highlight the effectiveness of the CPF-based Frank aggregation operators in evaluating renewable energy resources, providing decision-makers with a comprehensive and reliable framework for renewable energy planning and decision-making.

Neutrality geometric aggregation operators based on cubic q-rung orthopair fuzzy sets and their applications in multiple attribute group decision-making problems

Cubic q-rung orthopair fuzzy set (Cq-ROFS) is a hybrid set that can hold much more information to handle data uncertainties. It provides more space for decision makers (DMs) to describe their opinion in the multi-attribute group decision-making (MAGDM). In order to involve neutral character to discuss the neutral values assigned by decision-makers, in this paper, some cubic q-rung orthopair fuzzy aggregation operators based on neutrality operation are proposed, such as the cubic q-rung orthopair fuzzy weighted neutrality geometric (Cq-ROFWNG) operator, the cubic q-rung orthopair fuzzy ordered weighted neutrality geometric (Cq-ROFOWNG) operator, the cubic q-rung orthopair fuzzy hybrid neutrality geometric (Cq-ROFHNG) operator. Meanwhile, some properties of these operators are discussed. Further, we develop a novel MAGDM approach based on the proposed operators in a cubic q-rung orthopair fuzzy environment. Finally, numerical examples are provided to demonstrate the effectiveness and superiority of the proposed method through a detailed comparison with existing methods.

Consensus model for probabilistic linguistic multi-attribute group decision-making based on incomplete social trust networks

PurposeExperts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).Design/methodology/approachIn this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.FindingsThis study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.Originality/valueThis paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

A spherical Z-number multi-attribute group decision making model based on the prospect theory and GLDS method

Multi-attribute group decision-making is an important research field in decision science, and its theories and methods have been widely applied to engineering, economics and management. However, as the information embedded volume and complexity of decision-making expand, the diversity and heterogeneity of decision-making groups present significant challenges to the decision-making process. In order to effectively address these challenges, this paper defines the concept of spherical Z-number, which is a fuzzy number that takes into account a wide range of evaluation and its reliability. Additionally, a group decision-making model in a spherical Z-number environment is proposed. First, an objective phased tracking method is used to determine expert weights, maintain the consistency in decision-making group evaluations. The gained and lost dominance score method is combined with prospect theory to integrate expert psychological behavior when facing risks. The proposed method considers both group utility and individual regret, and balances the gains and losses of various options in the decision-making process. Finally, in response to the 3R principle, the model is employed to address the shared e-bike recycling supplier selection problem and to assess the viability of the decision-making outcomes. The results demonstrate that the model is robust in the context of varying parameter configurations. Moreover, the correlation coefficients between its ranking outcomes and those of alternative methodologies are all above 0.77, and its average superiority degree is 1.121, which is considerably higher than that of other methods. Consequently, the model's effectiveness and superiority are substantiated.

Time-series bidirectional adjustable N-soft expert MABAC method and its application for multi-attribute group decision-making

In hybrid models of soft expert sets, experts express only agreed or disagreed opinions about existing grades. This paper proposes a time-series bidirectional adjustable N-soft expert set model to address the shortcomings of existing models that cannot adjust existing grades to a more reasonable state or describe decision problems involving different times. Firstly, this model can explain the experts’ uncertain opinions and make positive or negative adjustments about existing grades. Secondly, the model contains information about time, describes dynamic multi-attribute group decision-making problems and explores objects’ changes and developments over time. And some related operations and propositions are derived. In addition, a new method called the bidirectional adjustable N-soft expert MABAC (multi-attributive border approximation area comparison) is proposed. On the one hand, the proposed method uses deviation maximizing and exponential decay methods to determine the time weights, ensuring the reliability of the time weights. On the other hand, it ranks objects based on their distances from an approximate boundary region, limiting the unconditional compensation among attribute values. Finally, this paper presents an example to verify its effectiveness and reliability by results analysis, sensitivity analysis, and comparison analysis.

Multi-attribute group decision making based on p, q-quasirung orthopair fuzzy Yager prioritized weighted geometric aggregation operator of p, q-quasirung orthopair fuzzy numbers

Multi-attribute group decision making based on p, q-quasirung orthopair fuzzy Yager prioritized weighted geometric aggregation operator of p, q-quasirung orthopair fuzzy numbers

Uncertain scheduling potential of charging stations under multi-attribute uncertain charging decisions of electric vehicles

A method for analyzing the uncertain adjustability of charging stations (CSs) under the influence of multi-attribute group decision-makings of electric vehicles (EVs) is proposed. Firstly, considering physical constraints of parameters such as charging power and energy stored, a model for characterizing the schedulability of a single EV under different charging modes including rated power charging, adjustable charging, and flexible charging and discharging is constructed. Secondly, since the charging mode selection of EVs is a comprehensive decision considering charging cost, charging time and other attributes, and individual decision-making is often affected by group users, an EV charging mode selection probability model is developed based on the multi-attribute group decision-making theory. Further, scheduling potential boundary parameters of CSs affected by the scheduling potential of all EVs in the station are derived based on the Minkowski sum theory, and their probability characteristics are calculated by combining the charging mode selection probability of single EVs and the schedulability under each mode. Finally, case studies prove the superiority of the proposed theory.

A sequential multiple attribute three-way group decision-making method to heterogeneous MAGDM problems with unknown weight information

As a decision model to depict the human cognitive process, three-way decision (3WD) provides a reasonable semantic interpretation for solving practical multi-attribute group decision-making (MAGDM) problems. At the same time, there often exist different types of information due to the subjective and objective criteria in practical decision-making problems, thus it is necessary to develop some efficient frameworks to deal with the MAGDM problems with heterogeneous information. In this paper, based on 3WD theory and sequential strategy, we propose a sequential multiple attribute three-way group decision-making (SMA3WGD) method for the heterogeneous MAGDM problem with unknown weight information to achieve this goal. At first, we compute the weights of decision-makers by using the consensus degree between each of the decision-makers in a group. Then, we calculate the collective evaluation result of multiple decision-makers by utilizing the mathematical planning method with two objectives of minimum difference and high similarity between group evaluation values and individual evaluation values. Meanwhile, a nested sequence of the set of attributes is constructed according to the attribute values offered by decision-makers that have different degrees of consensus on different attributes. On this basis, a multilevel granular structure for the heterogeneous MAGDM problems is constructed, and then the compute of the cost functions and conditional probability is explored in a single level of granular structure, and the thresholds and three-way decision rules of alternatives are further determined depending on the principle of cost minimization. Furthermore, by using a sequential strategy, we obtain a sequential three-way decision result of the heterogeneous MAGDM problems under the multilevel granular structure. At last, the practicality and advantages of the suggested method are demonstrated via a blockchain service provider selection case study, a parameter sensitivity analysis along with a comparison analysis.

Key components identification of EMU complex system faults with interval intuitionistic fuzzy set and multi-attribute group decision-making based on FMECA method

With the continuous acceleration of high-speed railway, the high-voltage traction system of the EMU is an important part for ensuring the operation speed and safety. If the failure does not discontinued effectively, it will cause major dangerous accidents, so the key components identification of system is crucial. This paper focus on the contradictions of the expert evaluation information ambiguity, the difference of expert risk appetite and the rationality of risk priority number (RPN) calculation method in the traditional failure analysis method FMECA. The interval intuitionistic fuzzy set (IIFS) is introduced to transform the expert evaluation into the form of membership interval and non-membership interval, which reduced the ambiguity of the specific numerical score. The interval intuitive fuzzy entropy was used to determine the entropy values of the occurrence (O), severity (S), and undetectable degree (D) of each failure mode under each expert score, which was used to calculate the weight value [Formula: see text], to weaken the influence caused by subjective risk preference. The interval intuition fuzzy ensemble operator (AIVIFWM) is used to assemble a single scoring matrix into a comprehensive score, which weakens the subjective influence of expert evaluation. Combined with the multi-attribute group decision-making idea, the score function [Formula: see text] is calculated for each comprehensive evaluation interval of each failure mode after assembly, so as to sort the failure mode risk and finally identify the key components. Based on the fault data of the high-voltage traction system of a certain type of EMU in 2022, 39 failure modes of 30 components are researched and summarized. The results show that rectifier, converter cooling unit, and carbon skateboard are the key components of EMU high-voltage traction system, which provided basic support for the detection and maintenance decision.

Assessment of bio-medical waste disposal techniques using interval-valued q-rung orthopair fuzzy soft set based EDAS method

Selecting an optimum technique for disposing of biomedical waste is a frequently observed obstacle in multi-attribute group decision-making (MAGDM) problems. The MAGDM is commonly applied to tackle decision-making states originated by obscurity and vagueness. The interval-valued q-rung orthopair fuzzy soft set is a novel variant of fuzzy sets. The main objective of this study is to introduce the interval-valued q-rung orthopair fuzzy soft Einstein-ordered weighted and Einstein hybrid weighted aggregation operators. Based on developed aggregation operators, a novel decision-making approach, the Evaluation based on the Distance from the Average Solution introduced to solve the MAGDM problem. The execution of the proposed approach demonstrates the significant impact of determining the most effective strategy to handle biomedical waste. Our proposed approach's practicality is confirmed by a case study focusing on selecting the most effective technique for Biomedical Waste (BMW) treatment. This study shows that autoclaving is the most effective method for the disposal of BMW. Comparative and sensitivity analysis confirms the consistency and effectiveness of our methodology. The comparative study indicates the effects of the proposed strategy are more feasible and realistic than the prevailing techniques.

Bounded rationality consensus reaching process with prospect theory and preventing individual weight manipulation for multi-attribute group decision making

Owing to the development and popularization of social media, multi-attribute group decision-making (MAGDM) adopting social network analysis (SNA) has appealed to widespread focus for a few years. However, most existing research on SNA has not simultaneously considered the expert's weight manipulation behavior and bounded rationality during the consensus-reaching process (CRP). To overcome this limitation, an original consensus model predicated on prospect theory (PT) and preventing individual weight manipulation is proposed in this paper. First, the experts’ behavior under certain risks is characterized by using PT, where a dynamic reference point is proposed to provide a more objective description of the experts’ psychological behavior. Then, the PT is integrated into the CRP, and the three-level prospect consensus measurement and a three-stage feedback identification mechanism are investigated. Subsequently, to safeguard against individual weight manipulation and obtain the appropriate expert weights, a feedback model incorporating minimum adjustment and maximum entropy rooted in PT is developed. This model is established from the perspectives of “efficiency” and “equity.” At last, the proposed method is applied to evaluate low-carbon investment projects in a power generation enterprise to verify its effectiveness and applicability. The superiority and robustness of the proposed method are also demonstrated through comparative analysis and sensitivity analysis.

Linguistic hesitant fuzzy interactive multi-attribute group decision making for enterprise resource planning selection

Enterprise resource planning (ERP) system selection involves multiple evaluation attributes with interaction. It can be attributed to a type of interactive multi-attribute group decision making (MAGDM). Linguistic hesitant fuzzy sets (LHFSs) are powerful tools to represent the uncertainty, hesitancy, and inconsistency of decision makers’ (DMs’) preference. This article proposes two new methods for interactive MAGDM with LHFSs based on comprehensive cloud (CC) power geometric (PG) aggregation operators. First, the CC of LHFS is defined and a distance measure between two CCs is offered. Considering the interaction among the aggregated LHFSs, we develop some CC PG aggregation operators of LHFSs. An uncertainty degree of LHFS is defined. Then, an approach is developed to derive the weights of DMs. An approach is proposed to derive the comprehensive attribute weights. Thus two new methods are presented for interactive MAGDM with LHFSs. An ERP selection example is provided to validate the proposed methods.

Electricity Sales Package Decision Making Using Two-Stage Density Clustering and Minimum Adjustment Distance Consensus

In the decision making of electricity sales packages, it is usually the specific situation of similar customers that provides the basis of a decision-making plan for target customer package selection, so it is particularly important to integrate the opinions of similar customers. Therefore, a multi-attribute group decision-making method for an electricity sales package is proposed, which is based on two-stage density clustering (TSDC) and minimum adjustment distance consensus. Firstly, in order to provide support for identifying similar customers among target customers, a sample customer set clustering method is proposed, which is based on a customer portrait label system and TSDC. Secondly, based on the entropy method, the attribute weight of the electricity sales package is determined. Based on the weight and the multi-attribute group decision-making consensus process, the minimum adjustment distance consensus of the sample customers’ fuzzy evaluation matrix for the electricity sales package is proposed. Then, a full-ranking decision method for an electricity sales package based on target customer satisfaction is proposed. Finally, customers in a certain area of China are selected as an example. This example is used to verify the accuracy and effectiveness of the decision-making method of electricity sales package proposed in this paper.

A generalized TODIM evaluation approach based on the novel score function and trust network under interval-valued hesitant fuzzy environment

In multi-attribute group decision-making (MAGDM), the social relationships among group members, professional backgrounds and psychological behaviors of decision makers are important factors for achieving high-quality group decisions. However, realizing the consideration of these factors in fuzzy and complex group decision-making scenarios is an important and urgent issue. In MAGDM problems, interval-valued hesitant fuzzy sets (IVHFSs) can better show the hesitant and fuzzy uncertainty information because they are an extension of fuzzy sets. This paper focuses on the score function of IVHFSs and develops a generalized TODIM evaluation method utilizing the novel score functions of IVHFEs and trust networks. The developed method effectively realizes the application of these factors in fuzzy and complex group decision making scenarios. In this contribution, a new deviation function for IVHFSs is created in this paper. It takes into account two types of deviation that are common in IVHFSs: the ambiguity of interval width and the hesitation and uncertainty of multiple intervals. In the sequel, a novel score function for IVHFSs is developed with help of the novel deviation function and the traditional score function. Concerning the perspective of group consistency, a model for determining expert weights is constructed using quality parameters, which combine the novel score function with the trust network. A subjective and objective comprehensive weighting model is made to get attribute weights based on how certain decision information is and the experts’ subjective experience. This is done because the more uncertain something is, the higher the risk. Eventually, we develop a generalized TODIM technique for MAGDM based on the novel score function and trust network in IVHFS environment. The developed approach is demonstrated with a preventive engineering decision-making problem. Sensitivity and comparative analyses are implemented to demonstrate the superiority, stability, and validity of the proposed method in this paper. According to the findings of this study, we conclude that the proposed methodology offers a comprehensive and systematic approach to evaluate MAGDM in the context of IVHFS with trust networks.

Self-confidence and consensus-based group decision making methods and applications

As the field of intelligent decision making continues to evolve, research on multi-attribute group decision making (MAGDM) is advancing at a steady pace. Nevertheless, existing research seldom addresses the aggregated solution of self-confidence and consensus in decision making information with diverse possibilities, despite its relevance to uncertain decision making environments. Consequently, this paper presents an approach to self-confidence and consensus-based group decision making. Firstly, the method provides a way for transforming three-parameter interval grey numbers into randomized decision making information. This establishes the foundation for the computation of possibility decision making information for uncertain decisions. Then, a method based on self-confidence and consensus is developed to determine the decision makers (DMs) weights, which is derived from the randomized decision making information. Next, a novel self-confidence index (SCI) for computing self-confidence and a new group consensus index (GCI) for computing consensus are demonstrated, and a reliability index (REI) method for randomized decision making information is given, which is used to identify high self-confidence and high consensus decision making information to improve the quality of decisions. Subsequently, a possibility ranking method is constructed based on pairwise comparisons of alternatives, taking into account density preferences, in order to obtain final possibility ranking conclusions. Finally, an illustrative example is provided to illustrate the concrete implementation process of the methods employed in this research. The efficacy of the proposed method is also demonstrated through a comparative analysis with the other methods.

Evaluation and selection of E-learning websites using intuitionistic fuzzy confidence level based Dombi aggregation operators with unknown weight information

In recent years, the surge in online learning has eclipsed traditional classroom teaching, elevating the prominence of e-learning platforms. However, the quest for a reliable methodology to evaluate these platforms has remained pressing. While several studies have explored this domain, none have integrated decision experts’ confidence levels. This study bridges this gap by seamlessly incorporating confidence levels into Dombi aggregation operators within an intuitionistic fuzzy framework. The study introduces two innovative operators: the confidence-level intuitionistic fuzzy Dombi weighted averaging operator (CIFDWA) and the confidence-level intuitionistic fuzzy Dombi weighted geometric operator (CIFDWG). These operators are employed to form a novel multi-attribute group decision-making (MAGDM) approach, which is demonstrated by applying them to select the optimal e-learning website in a real-world scenario. The “Stepwise Weight Assessment Ratio Analysis (SWARA)” method is utilized to determine criteria weights. The findings highlight the critical importance of sub-criteria such as topic diversity, pricing, content credibility, data security, and user interface simplicity. The analysis reveals Unacademy as the leading platform, closely followed by Byju’s. Through meticulous comparative analysis and sensitivity evaluations, we underscore the stability and coherence of the proposed model. The study’s novelty lies in its innovative approach to integrating confidence levels in multi-attribute group decision-making scenarios, promising broader applications across various real-world contexts. Educational advisers and management can leverage this methodology to empower students with informed choices.

A PSO-algorithm-based dual consensus method for large-scale group decision making and its application in medical imaging equipment purchasing

The increasing complexity of social activities requires an expanding number of people to be involved in decision-making, so that the large-scale multi-attribute group decision-making problems have gained widespread attention. After overviewing the researches of large-scale multi-attribute group decision-making methods, we have found that: The existing methods only consider consensus based on evaluation information of alternatives, while ignoring the consensus on importance of attributes. Thus, in order to tackle the issues and describe the hesitancy of decision makers in the decision-making process, this paper proposes a novel dual consensus method in large-scale multi-attribute group decision making under hesitant fuzzy linguistic environment. In the consensus reaching process, the method considers not only the consensus on the evaluation information of alternatives, but also the consensus on the importance of attributes, where both consensus reaching processes are implemented by the particle swarm optimization algorithm. The subjective weights of attributes are derived from the consensus reaching process of preference matrices, and the objective weights of attributes are obtained from the consensus reaching process of decision matrices, so as to acquire the comprehensive weights. After that, the overall ranking of alternatives can be obtained by TODIM. Finally, the proposed method is applied to a case study of medical imaging equipment purchasing decision-making, and the comprehensive analysis is provided to clarify advantages of the proposed method.