Group Decision-making Approach Research Articles

Sustainable strategies based on the social responsibility of the beverage industry companies for the circular supply chain

Circular supply chain management (CSCM) offers a fresh approach to enhancing supply chain sustainability and minimizing waste. A significant amount of waste is generated daily in the beverage industry, primarily due to the extensive production of beverages. The increasing waste amount causes the destruction of the environment and many problems for human life. The social responsibility of the beverage industry dictates that social and environmental performances are combined with the company’s economic performance to benefit society and the environment. The purpose of this research is to evaluate the effective factors and provide sustainable solutions for waste management in the CSCM of beverage industry companies based on their social responsibility. So in this regard, six sustainable strategies are suggested and evaluated based on the circular design, biodegradable packaging, product and manufacturer’s responsibility, critical success factors, and corporate social responsibility criteria, and theirs 24 sub-criteria for improving CSCM in the beverage industry. A novel group decision-making approach is proposed by developing the base-criterion method (BCM) and multi-attribute border approximation area comparison (MABAC) under fuzzy Z-extended numbers in order to evaluate the criteria weights and the rank of the strategies. A comprehensive managerial sensitivity analysis was performed better to understand the impact of different criteria for each strategy. The results show that cooperation with charities to return the waste and spend the added value created to help the needy fulfill the company’s social responsibility is one of the most important strategies specified by decision-makers and experts for improving the CSCM of beverage industry.

Multiple-attribute group decision-making approach using power aggregation operators with CRITIC-WASPAS method under 2-dimensional linguistic intuitionistic fuzzy framework

The 2-dimensional linguistic intuitionistic fuzzy variables (2DLIFVs) provide an efficient tool to model the cognitive information of experts in practical circumstances while considering the reliability of the evaluation results. It is a hybrid model representing qualitative information, which derives from integrating the 2-dimensional linguistic variables (2DLVs) with linguistic intuitionistic fuzzy numbers (LIFNs). In this work, we first define a novel score function to establish a proper ranking order among the 2DLIFVs. Next, we define a new distance measure under a 2-dimensional linguistic intuitionistic fuzzy framework to determine the difference between 2DLIFVs. Some mathematical properties and special cases of the defined distance measure are also discussed. The power average (PA) operators provide aggregation tools, allowing aggregated arguments to support each other in the aggregation process. Motivated by the notion of power average (PA) and power geometric (PG), we define four new aggregation operators (AOs) to aggregate 2DLIFVs by considering information correlation in terms of support degree during the aggregation process. Several mathematical properties of the proposed AOs are also investigated. In addition, a new integrated 2DLIF-CRITIC-WASPAS methodology is developed in the 2-dimensional linguistic intuitionistic fuzzy environment to deal with complex decision issues. In this method, a “Criteria Importance Through Intercriteria Correlation (CRITIC)” approach is used to obtain the associate weights of the attributes, and the “Weighted Aggregated Sum Product Assessment (WASPAS)” method is executed to establish a valid ranking order among the alternatives.Moreover, a real problem of venture capital investment related to renewable energy projects is considered to demonstrate the applicability of the proposed methodology. The evaluation process considers four aspects: the management team, service or product, finance, and the market, which are related to 14 attributes. A sensitivity analysis is carried out to explain the reliability of the obtained results. Comparing the proposed method with some extant methods establishes the strength and robustness of the formulated method in real-world situations.

Double-layer multi-criteria group decision-making approach using neutralized possibility degree-based decision matrix with fuzzy information

Multi-criteria group decision-making (MCGDM) problems are fundamentally characterized by uncertainty and ambiguity. The quantification of such data is vital for determining the most optimal course of action. In this paper, a double-layer possibility degree-based interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs) Vlsekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) approach is presented. First, the possibility degrees of IVq-ROFSs are calculated and processed to establish a new neutralized decision matrix. Second, the components of the new decision matrix are taken into account for obtaining a new pair of positive and negative ideal solutions. The utility and regret measures in the VIKOR approach are ultimately determined by combining criteria weights and cumulative possibility degrees to rank the alternatives. The results of the study show that the proposed approach contributes to acquiring accurate alternative rankings in situations where traditional approaches have had difficulty generating ranks. Also, by eliminating the early normalization stage, which has a direct impact on the final ranking, the proposed approach concludes a unique ranking for each decision-making problem. Several comparative and sensitivity analysis are also conducted to exhibit robustness and applicability of the proposed approach in real world.

Improving Analytic Hierarchy Process inside the Analytic Group Decision-Making Approach Method with Two-Dimensional Cloud Model for Water Resource Pollution Risk Warning Evaluation: A Case Study in Shandong Province, China

This study proposes a water resource pollution risk warning evaluation method. Firstly, an evaluation system is constructed, consisting of 15 secondary indicators in four aspects: water quality, ecology, utilization protection, and water disasters. Then, an improved AGA-AHP method and coefficient of variation method are used to determine the weights of each indicator. Cloud models are employed to describe the characteristics of standard clouds and evaluation clouds, establishing a two-dimensional cloud model with risk probability and hazard level as variables. Taking a certain region in Shandong Province, China, as an example, the quantitative analysis results indicate that the water pollution risk level in the area is classified as Level IV, with particular attention needed for water quality and management indicators. Simultaneously, a series of measures such as source control, monitoring and early warning, emergency response, and public participation are proposed to further reduce the risk. The research findings demonstrate the following: (1) The establishment of a comprehensive indicator system for multidimensional assessment; (2) The combination of the AGA-AHP method and cloud model for quantitative analysis; (3) The practicality of the method validated through the case study; (4) Providing a basis for subsequent decision-making. This study provides new insights for water environmental risk management, but a further optimization of the model to enhance predictive capability is required when applied in practical scenarios. Nevertheless, the preliminary validation of this method’s application prospects in water resource risk monitoring has been achieved.

An integrated picture fuzzy operational competitiveness ratings group decision approach for evaluating the enterprise digital transformation capability

An integrated picture fuzzy operational competitiveness ratings group decision approach for evaluating the enterprise digital transformation capability

An extended multi-expert concept lattice-based heterogeneous multi-attribute group decision-making approach

In practical multi-attribute group decision-making (MAGDM) problems, it is common to utilize heterogeneous representation forms to express distinct preference information for different experts, primarily due to their diverse backgrounds. To address the problem of reducing information loss from the aggregation of experts' opinions in heterogeneous MAGDM as well as improving the interpretability of the decision-making process, this paper introduces a concept lattice-based heterogeneous MAGDM approach. The heterogeneous multi-expert formal context is first proposed to capture the heterogeneous evaluation information of alternatives provided by different experts. Then, extended multi-expert concept lattices are constructed to aggregate evaluation information of alternatives by different experts. In this case, all concepts are considered to minimize information loss during the aggregation process. Second, to obtain more reasonable decision results, the distance between the concept intents and the heterogeneous positive and negative ideal solutions is considered, and the alternatives are ranked based on this measure. In addition, the MAGDM process for each alternative is visualized using the extended multi-expert concept lattices. This representation aids in identifying key concepts, their interdependencies, and the overall impact on the decision result. Finally, numerical examples and comparative analysis validate the validity and rationality of the proposed approach in heterogeneous MAGDM.

Parents’ acceptance attitudes towards the vaccination of children based on M-LSGDM approach in China: a cross-sectional study

ObjectivesEnsuring that children receive timely vaccinations is paramount for preventing infectious diseases, and parental attitude plays a pivotal role in this process. This study addresses this gap in the existing...

Research on Nuclear Power Plant Operational Task Complexity Assessment Method Based on Group Decision Making and Deep Learning of EEG Signals

This study aims to explore the correlation between the operational task complexity of nuclear power plant (NPP) operators and electroencephalogram (EEG) features. Initially, we segmented EEG signals according to operational steps and extracted a total of 120 time domain, frequency domain, and time-frequency domain features. Subsequently, we applied an adaptive principal component analysis (PCA) dimensionality reduction method to process the features. On the other hand, three experts were invited to evaluate the complexity of the operational tasks, and their evaluation data were synthesized using a group decision-making approach. A correlation analysis was performed between these data and the PCA-reduced feature data, identifying the features with the highest correlation coefficient for each participant. Then we built a long short-term memory model with the data of the first group of participants to predict the task complexity value and tested it with the data of the second group of participants. Testing the model with data from the second group yielded favorable results, with a training set mean squared error (MSE) of 0.025 and a testing set MSE of 0.078. The results of this study indicate a significant correlation between specific EEG features and task complexity in the operational tasks of NPP operators. The model established through a combination of group decision making and machine learning methods effectively predicted the task complexity levels for operators in different operational tasks. This research provides a new perspective on NPP operators’ cognitive load and operational tasks, holding practical significance for operator training and workload management.

An approach of multi-criteria group decision making with incomplete information based on formal concept analysis and rough set

Decision-making problem is a fundamental activity in human life every day. Regarding group decision-making, individuals have insufficient comprehension of the decision objects owing to the limited experience and knowledge and drawback the evaluation process. Therefore, the evaluation information is missing and the group cannot get the precise information for decision objects resulting in the generation of incomplete information systems. It is meaningful for individuals finding an approach to Leverage and tap into existing information. On the basis, the work presented in this paper focuses on group decision-making problems (GDM) with incomplete information. Under the framework of group decision-making, a GDM approach with an incomplete information system is presented combining the formal concept analysis and rough set theory. Firstly, we propose a concept called formal decision context and discuss the properties of the formal decision context, which could divide the incomplete information system into many cognitive units, and the definitions of cognitive units will be given. Then, the property and the relations of various cognitive units will be discussed. Additionally, the upper and lower approximation space under the formal decision context is founded considering the risk attitudes integrating fuzzy rough set and formal concept context. Considering the order relation of various cognitive units and decision objects, we introduce the dominance relation and the importance degree of each cognitive unit under the framework of formal decision context. Furthermore, a theoretical framework of the multi-criteria decision-making problem is presented and two models for the multi-criteria group decision-making problem are discussed combined with fuzzy rough set and formal decision context from two perspectives, including qualitative analysis and quantitative analysis. Finally, an illustrated example is applied to verify the validity and effectiveness of the proposed models. The contributions of this paper include two aspects. On the one hand, this paper provides a new perspective and paradigm considering MCGDM problems with the incomplete information system from the perspective of group consensus. On the other hand, this paper gives two approaches for group decision-making problems from sorting and set theory by the perspective of multi-granular rough set and group consensus.

A large-scale group decision-making approach based on hesitancy degrees and non-cooperative behaviors with picture fuzzy information

Large-scale group decision-making (LSGDM) problems involve two processes: a clustering process is first implemented to break down a larger group into several smaller subgroups for simplification purposes, after which a consensus reaching process (CRP) is utilized to eliminate information conflicts among large-scale decision-makers (DMs). Opinion conflicts are inevitable in large DM groups due to self-interest biases and information blind spots. To address these limitations, this study proposes a twofold feedback mechanism to address both non-cooperative behaviors and information hesitation. Firstly, based on detailed review of existing similarity measures, we defined a novel picture fuzzy Chi-square similarity measure (NPFCS) to enhance accuracy. To distinguish subgroups, the second procedure was developed to establish a weight assignment model following the implementation of the developed similarity-based fuzzy clustering algorithm. In the third step, decision opinions are examined from two distinct perspectives: the hesitancy degrees and non-cooperative behaviors, to ensure favorable decision outcomes. Alongside the definition of a hierarchical mechanism for detecting DMs with information blind spots, the identified DMs were mandated to resubmit individual matrices. This measure aims to prevent the aggregation of negative impacts on information credibility caused by the major principle. Furthermore, non-cooperative behaviors were identified at the element level of multi-criteria matrices with corresponding adjustments performed automatically. Next, a real-world site selection problem involving thirty DMs was solved using the proposed method to illustrate its application. Moreover, the results of sensitivity analysis demonstrate that our proposal is robust to changes in model parameters, and several comparisons were conducted from two perspectives, thus confirming the superiority of the proposed approach.

Design concept evaluation based on cloud rough model and modified AHP-VIKOR: An application to lithography tool manufacturing process

Design concept evaluation is a wide domain of research as the production process of new product development is mainly based on the evaluations of experts. As different types of uncertainties like vagueness, randomness, and diversity essentially exist in the evaluation information, the manipulation models that deal with just one type of uncertainty are not appropriate for ranking the design alternatives. The integration of multiple approaches into a single mathematical model to create an effective framework is more useful for uncertain information manipulation. This paper presents an innovative group decision-making approach to evaluate design concepts by integrating cloud rough numbers, analytic hierarchy process (AHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to introduce a novel cloud rough AHP-VIKOR model. The concept of cloud rough numbers is utilized by fusing the merits of the cloud model theory and rough approximations in addressing randomness in experts’ judgments and intrapersonal uncertainty for handling consensus inconsistency. Some new modified algebraic operations are introduced to remove the errors in already existing operations of cloud rough numbers. The cloud rough AHP technique is introduced to compute weights of design criteria by computing the cloud rough numbers of linguistic values and their reciprocals. Based on cloud rough values of positive and negative ideals, the individual regret, group utility values and evaluation indices are determined. Using the distance of evaluation indices from minimized cloud rough value, the design alternatives are ranked using different formulae. The significance of the proposed approach is illustrated with a real-world application in lithography tool manufacturing process. The out-performance and reliability of the proposed model is studied by a sensitivity analysis of different parameters in the method, and a comparison analysis with existing approaches.

A novel incomplete hesitant fuzzy information supplement and clustering method for large-scale group decision-making.

Clustering is an effective means to reduce the scaling of large-scale group decision-making (LSGDM). However, there are many problems with clustering methods, such as incomplete or ambiguous information usually provided by different decision makers. Traditional clustering methods may not be able to handle these situations effectively, resulting in incomplete decision-making information. Calculating the clustering centers may become very complex and time-consuming. Inappropriate distance weights may also lead to incorrect cluster assignments, and these problems will seriously affect the clustering results. This research provides a novel incomplete hesitant fuzzy information supplement and clustering approach for large-scale group decision-making in order to address the aforementioned difficulties. First, the approach takes into account the trust degradation and the inhibition of relationships of distrust in the process of trust propagation, and then it builds a global and local network of trust. A novel supplemental formula is provided that takes into account the decision-preference maker's as well as the trust-neighbor's information, allowing the decision-neighbor maker's recommendation to be realized. Therefore, an improved distance function can be proposed to calculate the weights by combining the relative standard deviation theory and selecting the selected clustering centers by using the density peaks in order to optimize the selection of clustering centers and reduce the complexity and scaling of the decision. Finally, an example is presented to demonstrate how the proposed method can be applied. The consistency index and comparison experiments are used to evaluate if the suggested approach is effective and reliable.

Evaluation of Propulsion Technologies for Sustainable Road Freight Distribution Using a Dual Probabilistic Linguistic Group Decision-Making Approach

For the first time, this study addresses the problem of evaluating propulsion technologies for sustainable road freight distribution from the multicriteria group decision-making perspective. It aims to help logistics companies involved in global supply chains significantly reduce GHG emissions from freight distribution activities. To evaluate sustainable propulsion technologies, this article introduces a novel group decision-making approach with DPL term sets, which takes into account the simultaneous occurrence of stochastic and nonstochastic uncertainty. Since the existing methods do not consider the dependency among multiple numbers of criteria, the evaluation results become insignificant. To overcome this, we employ the Archimedean operations on DPL elements to develop DPL weighted Archimedean Maclaurin symmetric mean operator. For measuring the weights of criteria, an optimization model using a cross-entropy measure of DPL elements is developed. A case study for one of the major logistics companies in Serbia is presented to exhibit the relevance of the suggested approach, as well as offer decision-making guidelines in real-world settings. The research findings show that hybrid electric trucks are the most advantageous propulsion technology solution to reduce GHG emissions from distribution activities. The superiority of the proposed approach is demonstrated through the comparative investigation.

A Novel Group Decision-Making Approach With Entropy Measure for Energy Aware in Wireless Sensor Network Architecture

A Novel Group Decision-Making Approach With Entropy Measure for Energy Aware in Wireless Sensor Network Architecture

Neutrosophic VIKOR approach for multi-attribute group decision-making

Uncertainty, imprecise, incomplete, and inconsistent information can be found in many real-life systems and may cause more complex problems. A neutrosophic set is an effective and useful tool to describe problems with Uncertainty, imprecise, incomplete, and inconsistent information. The neutrosophic set is characterized by three independent degrees namely the truth-membership degree (T), indeterminacy-membership degree (I), and falsity-membership degree (F). In this paper, we present an extension of the VIKOR method for the solution of multi-criteria decision-making problems, namely neutrosophic set-VIKOR (NS-VIKOR) in a refined neutrosophic environment. The weight of each decision-maker is considered a single valued neutrosophic number. The criteria for the weight of every decision-maker are also considered neutrosophic numbers. An aggregation operator is used to combine all decision-makers’ opinions into a single opinion for a rating between criteria and alternatives. Euclidean distances from the positive and negative ideal solutions are calculated to construct relative closeness coefficients. Lastly, an illustrative example of tablet selection is provided to show the applicability of the proposed VIKOR approach.

A Group Decision-Making Approach Leveraging Preference Relations Derived from Large Language Model

Group decision-making involves selecting among limited options. Experts share their perspectives in a comparative debate but then evaluate alternatives using preference relations, which can result in inconsistencies between their expressions and assessments. To address this, a method is proposed that automates the generation of these relationships from the debate comments, classifying them into positive and negative using sentiment analysis, namely with the Large Language Model. A new operator is introduced that weights these comments to calculate preference relations. Furthermore, modification of the relationships is allowed if the experts so wish. Moreover, another operator is incorporated that adjusts the weight of each expert according to his or her active participation in the discussion, assigning more weight to those who contribute more comments. Finally, this innovative method promotes coherence and equal participation in group decision-making by employing an innovative sentiment analysis detection system.

Novel linguistic $ q $-rung orthopair fuzzy Aczel-Alsina aggregation operators for group decision-making with applications

<p>In this article, we presented two novel approaches for group decision-making (GDM) that were derived from the initiated linguistic $ q $-rung orthopair fuzzy Aczel-Alsina weighted arithmetic (L$ q $-ROFAAWA) aggregation operator (AgOp) using linguistic $ q $-rung orthopair fuzzy numbers (L$ q $-ROFNs). To introduce these GDM techniques, we first defined new operational laws for L$ q $-ROFNs based on Aczel-Alsina $ t $-norm and $ t $-conorm. The developed scalar multiplication and addition operations of L$ q $-ROFNs addressed the limitations of operations when $ q = 1 $. The first proposed GDM methodology assumed that both experts' weights and attribute weights were fully known, while the second technique assumed that both sets of weights were entirely unknown. We also discussed properties of L$ q $-ROFNs under the L$ q $-ROFAAWA operators, such as idempotency, boundedness, and monotonicity. Furthermore, we solved problems related to environmental and economic issues, such as ranking countries by air pollution, selecting the best company for bank investments, and choosing the best electric vehicle design. Finally, we validated the proposed GDM approaches using three validity tests and performed a sensitivity analysis to compare them with preexisting models.</p>

3D Printer Selection for the Sustainable Manufacturing Industry Using an Integrated Decision-Making Model Based on Dombi Operators in the Fermatean Fuzzy Environment

Three-dimensional printers (3DPs), as critical parts of additive manufacturing (AM), are state-of-the-art technologies that can help practitioners with digital transformation in production processes. Three-dimensional printer performance mostly depends on good integration with artificial intelligence (AI) to outperform humans in overcoming complex tasks using 3DPs equipped with AI technology, particularly in producing an object with no smooth surface and a standard geometric shape. Hence, 3DPs also provide an opportunity to improve engineering applications in manufacturing processes. As a result, AM can create more sustainable production systems, protect the environment, and reduce external costs arising from industries’ production activities. Nonetheless, practitioners do not have sufficient willingness since this kind of transformation in production processes is a crucial and irrevocable decision requiring vast knowledge and experience. Thus, presenting a methodological frame and a roadmap may help decision-makers take more responsibility for accelerating the digital transformation of production processes. The current study aims to fill the literature’s critical theoretical and managerial gaps. Therefore, it suggests a powerful and efficient decision model for solving 3DP selection problems for industries. The suggested hybrid FF model combines the Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF–SWARA) and the Fermatean Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (FF–RAFSI) approaches. The novel FF framework is employed to solve a critical problem encountered in the automobile manufacturing industry with the help of two related case studies. In addition, the criteria are identified and categorized regarding their influence degrees using a group decision approach based on an extended form of the Delphi with the aid of the Fermatean fuzzy sets. According to the conclusions of the analysis, the criteria “Accuracy” and “Quality” are the most effective measures. Also, the suggested hybrid model and its outcomes were tested by executing robustness and validation checks. The results of the analyses prove that the suggested integrated framework is a robust and practical decision-making tool.

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.

Multi-criteria group decision making based on graph neural networks in Pythagorean fuzzy environment

Given that the majority of existing approaches for multi-criteria group decision making (MCGDM) rely solely on the preferences of decision makers (DMs) and fail to consider the various relationships between alternatives, this paper attempts to model the relevant relational structures using graphs and introduce the concept of graph neural networks (GNNs) in the context of group decision-making. By leveraging the powerful expressive capabilities of GNNs, the aim is to mine additional information pertinent to the decision-making process and screen out alternatives for the final decision. To begin, we provide a mapping of MCGDM to the graph domain and construct a corresponding relation graph among alternatives. Additionally, to deal with uncertain or vague information, we transform the group decision-making problem into a Pythagorean fuzzy environment and define a novel measure of entropy specifically designed for Pythagorean fuzzy sets (PFSs) in the entropy weight model to determine the weights of criteria. Simultaneously, we propose a new distance measure for PFSs, which is then applied to the extended Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to rank alternatives. Furthermore, we develop a GNNs-based Pythagorean fuzzy MCGDM approach that incorporates the aforementioned techniques for group decision-making. Finally, to validate the effectiveness and superiority of this approach, we employ it to address a supplier selection issue. Compared with baseline group decision-making approaches, our approach can indeed capture the relationships among alternatives in complex group decision-making scenarios and outperforms the best-performing baseline by nearly 2.8% in terms of ranking accuracy.