Group Decision-making Approach Research Articles

Group decision making based on weighted distance measure of linguistic intuitionistic fuzzy sets and the TOPSIS method

Linguistic intuitionistic fuzzy numbers (LIFNs) are useful to express the uncertainty of qualitative aspects of information, which have received many attentions in recent years. In this paper, we propose the distance measure of linguistic intuitionistic fuzzy sets (LIFSs), where the membership grade and the non-membership grade of each element in the universe of discourse belonging to a LIFS are represented by LIFNs. We also provide the proofs of the validity and some desirable properties of the proposed distance measure of LIFSs. Moreover, we also propose the weighted distance measure of LIFSs. Based on the proposed weighted distance measure of LIFSs and the “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) method, we propose a new group decision making (GDM) approach in the environments of LIFNs. We also use some examples to illustrate the practicability and the feasibility of the proposed GDM approach. The proposed GDM approach can overcome the drawbacks of the existing GDM approaches, where they have the drawbacks that they cannot distinguish the ranking orders of the alternatives in some situations. It provides us a very useful method for dealing with GDM problems in the environments of LIFNs.

Blockchain adoption barriers in Moroccan sustainable supply chain: a proposed approach

Currently, <span>the emerging countries like Morocco seeks to benefit from the potential of blockchain technology to meet its various growing demands, especially in sustainable supply chain management (SSCM). This explains the need for more effort to understand blockchain implementation and identify the barriers influencing the blockchain adoption decision in SSCM, especially, from Moroccan industry and service sectors perspective. In this context, this research paper proposes a group decision-making approach to identify the barriers from a comprehensive literature search, then evaluate them based on intuitionistic fuzzy analytic hierarchy process (IFAHP). Due to the varied importance of the selected barriers, IFAHP is utilized to allocate priority weights for each barrier according to its importance level. The evaluation results reveal that “Government policy and support” and “Challenges in integrating sustainable practices and blockchain technology through <a name="_Hlk106113265"></a>sustainable supply chain management (SCM)” are the best ranked barriers that impact the implementation of blockchain technology in Moroccan context. The main objective is to inquire the barriers preventing the blockchain implementation, and assist industry decision-makers in developing supple short- and long-term decision-making strategies for better sustainable supply chain management.</span>

A Hybrid Group Decision Approach Based on MARCOS and Regret Theory for Pharmaceutical Enterprises Assessment under a Single-Valued Neutrosophic Scenario

Evaluating pharmaceutical enterprises with sustainable and high-quality development ability (SHQDA) can not only provide strategies for the pharmaceutical management department in formulating enterprise development plans, but also provide suggestions and guidance for enterprises to enhance their core competitiveness. Nevertheless, the prior research possesses several deficiencies in coping with the assessment of enterprises with SHQDA under uncertain environments to predict the psychological behavior of the evaluator and the correlation among the evaluation criteria. To conquer the aforementioned defects, we propose an integrated framework for rating pharmaceutical enterprises that incorporates regret theory, measurement alternatives and ranking based on the compromise solution (MARCOS) and Heronian mean operating within a single-value neutrosophic set (SVNS) environment. First, the single-valued neutrosophic number (SVNN) is employed to portray the assessment information of experts. Then, a novel single-valued neutrosophic score function is presented to enhance the rationality of the SVNN comparison. Next, a combined criteria weight model is constructed by synthesizing the best and worst method (BWM) and criteria importance through intercriteria correlation (CRITIC) approach to attain more reasonable and credible weight information. Furthermore, the integrated assessment framework combining regret theory-MARCOS method and Heronian mean operator is put forward to assess and select the enterprises with SHQDA under a single-valued neutrosophic setting. Ultimately, an empirical concerning the pharmaceutical enterprises assessment is presented within SVNS to illustrate the usefulness and effectiveness of the presented SVNS regret theory-MARCOS method. Thereafter, the sensitivity analysis and comparison analysis are implemented to provide evidence for the rationality and superiority of the proposed method.

A Group Decision-Making Approach for Global Consistency of Heterogeneous Models

The design of complex systems goes through a multi-view paradigm in which separate teams, from different viewpoints, build partial source models describing the system. These source models are called heterogeneous models since they are expressed in different languages. The main objective of this paper is to provide an approach — called CAHM for Collaborative Alignment of Heterogeneous Models — that leverages collaborative engineering and especially group decision-making principles to ensure the overall consistency of heterogeneous source models. This approach defines two sub-processes: a first one to collaboratively match heterogeneous models to develop the inter-model correspondences and a second one ensuring the consistency of the produced model of correspondences in case of model evolution. In this paper, we restate the basis of the CAHM approach, then, we detail the second sub-process that aims at maintaining the coherence of the overall system. This sub-process handles the evolution of source models by managing the impact of these evolutions on the established model of correspondences. It incorporates mechanisms to calculate the impact of changes, as well as mechanisms to formalize the group decision-making, while addressing the inconsistencies that may occur due to changes. CAHM is illustrated and validated on a real example of a hospital emergency department case study.

A VIKOR-based group decision-making approach to software reliability evaluation

Software reliability evaluation is important to attribute of software quality. A new group decision-making is provided to evaluate software reliability, where the model is based on an extended VIsekriterijumska optimizacija i KOmpromisno Resenje technique. The individual utility and individual regret in general VIKOR technique are extended to group utility and group regret. A specific regret matrix is provided in new VIKOR-based GDM method. A new ranking method is provided in a static environment. Another new ranking method is provided in a dynamic environment. To implement a user-based evaluation for software reliability, the picture fuzzy set is used for handling the questionnaire information. It is noted that the existing projection measure is not always reasonable in picture fuzzy setting. To solve this problem, a new normalization projection measure is provided in picture fuzzy setting. And a new GDM method is established for software reliability evaluation. The feasibility and practicability developed method in this work are illustrated by an experimental analysis.

General circulation models for rainfall simulations: Performance assessment using complex networks

Reliable assessment of the impact of climate change on hydrology in a region requires proper selection of General Circulation Models (GCMs). The present study introduces the concepts of complex networks for evaluating the performance of GCMs in simulating rainfall and selecting an ensemble of the best-performing GCMs. The performance of 49 GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating monthly rainfall over India is assessed. The observed (interpolated and gridded rainfall provided by the India Meteorological Department) rainfall data and GCM-simulated rainfall data over the period 1961–2014 at a spatial resolution of 1° x 1° (a total of 288 grids) are studied. The GCMs are evaluated for two cases: (1) Case 1 – whole year rainfall; and (2) Case 2 – summer monsoon rainfall (June–September). The clustering coefficient of the rainfall network is used as a network measure to evaluate the ability of the GCMs in simulating rainfall. For rainfall network construction, each grid is considered as a network. The phase space reconstruction concept is used to reconstruct the single-variable rainfall time series in a multi-dimensional phase space to represent the rainfall dynamics. The optimal dimension for reconstruction is determined using the false nearest neighbor (FNN) algorithm. For network construction, each reconstructed vector is considered as a node, and the connections between them, identified using a distance threshold for the reconstructed vectors in the phase space, serve as the links. For each of the 288 grids, the GCMs are ranked based on the difference in the clustering coefficient between the observed and GCM-simulated rainfall networks. The group decision-making (GDM) approach is employed to identify the ensemble of the best-performing GCMs for the entire study area considering all the grids. The results suggest different models perform well for the whole-year rainfall and summer monsoon rainfall. For the whole-year rainfall, the models CMCC-CM2-HR4, GFDL-ESM4, CMCC-ESM2, EC-Earth3-AerChem, and CMCC-CM2-SR5 rank as the top five. For the summer monsoon rainfall, FIO-ESM-2-0, E3SM-1-1, CESM2-FV2, CMCC-CM2-HR4, and CMCC-CM2-SR5 perform the best. Considering the rank of each model for both the cases, the best-performing models are identified to be CMCC-CM2-HR4, CMCC-CM2-SR5, CMCC-ESM2, FIOESM-2-0, and E3SM-1-1. The rainfall simulated from these models also show close resemblance to the observed rainfall, especially in terms of monthly mean rainfall. Therefore, even among the many GCMs that show good agreement with the monthly mean observed rainfall, the clustering coefficient-based analysis helps to narrow down the models for the ensemble of best-performing GCMs.

A fuzzy multicriteria group decision approach for classification of failure modes in a hospital’s operating room

The operating room (OR) is one of the most important departments of any hospital. Mismanagement can lead to severe problems with patient’s safety and satisfaction, costs, and profitability. The Failure Mode and Effects Analysis (FMEA) is a risk assessment tool used to identify potential failure modes (FM) which should be prioritized to be mitigated. To assess the risks of an OR, some important aspects cannot be neglected: it should be conducted by a team of experts from different functional areas; address the non-compensation between criteria; classify FM, instead of ranking it, since it is easier to understand and support decision making; address the subjectivity present on experts’ opinion; and consider the mitigation complexity, since hospital’s resources are limited. However, were not found studies in the literature which simultaneously covered all these aspects. Therefore, this paper proposes a new group decision model, which combines a consensus reaching process with ELECTRE TRI and double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS). A pilot application of the model was conducted in an OR of a Brazilian hospital. The results have shown that the consensus technique, the mitigation complexity, and the non-compensation can modify the failure modes classification. Also, the DHHFLTS allowed the experts to use more complex linguistic terms to express their opinion. In addition, the pilot application was carried out during the covid-19 pandemic and the results have shown that due to the high number of elective surgeries being rescheduled to give space to urgent surgeries, the patients' health problems become even worst and they turn to urgent surgeries.

Three-Way Group Decisions with Incomplete Spherical Fuzzy Information for Treating Parkinson’s Disease Using IoMT Devices

As Internet of Things (IoT) is extensively employed in diverse realistic areas, it is vital to effectively and timely analyze the data collected by IoT devices. To cope with this problem, by integrating adjustable MG SF probabilistic rough sets (PRSs) with the TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method, this paper explores a three-way multi-attribute group decision-making (MAGDM) approach in the context of multigranulation (MG) spherical fuzzy (SF) incomplete information systems (IISs) and further applies the presented method to the analysis of leg muscle data obtained from Internet of Medical Things (IoMT) devices for Parkinson’s patients. First, the concept of MG SF IISs is established, and the completion method is provided. Then, adjustable MG SF PRSs are proposed for information fusion. Afterwards, considering the bounded rationality of decision-makers (DMs), a new three-way MAGDM method is designed by fusing adjustable MG SF PRSs with the TODIM method. Finally, in the context of IoMT-based detecting abnormal knee joints in Parkinson’s patients, the applicability and validity of the presented method are eventually verified.

A Rough-Fermatean DEMATEL Approach for Sustainable Development Evaluation for the Manufacturing Industry

With the increasing awareness of the importance of sustainable development, the evaluation of sustainability performance for the manufacturing has become an emerging issue of importance. Although many scholars have studied the issue of enterprise sustainability, to date, the institutional and risk control perspectives of sustainability have received less attention, particularly in manufacturing practices. To address research gap, this study develops a comprehensive framework for evaluating enterprise sustainability that encompasses the economic, social, environmental, institutional, and risk control dimensions. A novel group decision-making approach, called the Rough-Fermatean Fuzzy Decision-Making Trial and Evaluation Laboratory technique is proposed, which identifies the mutual relationships among the 15 evaluation criteria and generates the influence weights. Fermatean fuzzy theory is used to reflect the subjective judgment of experts and the inherent uncertainty in evaluation. Moreover, rough set theory is used to aggregate the multiple experts’ opinions and to retain potential information in the form of interval values. A case study of Taiwan’s electronics manufacturers is carried out to demonstrate the practicality and validity of the proposed approach. The results show that goodwill and corporate image, zero emission of pollutants and waste discharge, and corporate governance are the most influential sustainability criteria. This study can assist decision-makers in the manufacturing sector in developing more appropriate improvement strategies. Enterprises can improve their competitiveness and move towards sustainable development by first focusing on the more influential criteria. For academic researchers, the proposed framework and methodology can be applied to the discussion of sustainability evaluation in other industries.

A multi-criteria group decision-making approach based on revised distance measures under dual hesitant fuzzy setting with unknown weight information

A multi-criteria group decision-making approach based on revised distance measures under dual hesitant fuzzy setting with unknown weight information

A reliable GIS-based FAHP-FTOPSIS model to prioritize urban water supply management scenarios: A case study in semi-arid climate

• Developed a GIS-based-MCDM method to select optimum urban water management scenario. • The paper considers triangular fuzzy sets to reduce uncertainty in decision-making. • The developed model is robust and flexible to manage required urban water demands. • A hydrologic model utilizing WEAP is designed to assess the developed model scenarios. The growing problem of urban water shortage and its sustainable management methods is one of today's critical research needs globally. This paper applies an integration of the Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) with the help of triangular fuzzy sets to manage urban water supply priorities in a semi-arid region. Hence, a group decision-making approach was proposed by combining the Fuzzy AHP (FAHP) and the Fuzzy TOPSIS (FTOPSIS) models based on quantitative and qualitative criteria. Therefore, a hierarchical model-based GIS and AHP was developed to classify the effective criteria and determine the Importance Weights (IWs) of criteria according to the experts’ opinions and using data layers based on spatial criteria. The priority ranking of scenarios was obtained using the FTOPSIS method. Different criteria such as environmental, geographical, geological, economic, climatic, and social conditions were considered in this study. Furthermore, to assess the water supply scenarios under the essential water demands of the basin cities and suburbs, a hydrologic model utilizing WEAP software was designed. Among the considered criteria, total expense, climatic conditions, and geographic conditions with the IWs values of 0.9, 0.85, and 0.83 were determined as the most effective criteria for evaluating the scenarios' ranking, respectively.

A novel hybrid multi-criteria group decision-making approach with intuitionistic fuzzy sets to design reverse supply chains for COVID-19 medical waste recycling channels

The COVID-19 pandemic has led to exponential growth in COVID-19 medical waste (CMW) generation worldwide. This tremendous growth in CMW is a major transmission medium for COVID-19 virus and thus brings serious challenges to medical waste (MW) management. Designing an efficient and reliable CMW reverse supply chain in this situation can help to prevent epidemic spread. Nowadays, the assessment of CMW recycling channels has become a challenging mission for health-care institutions, especially in developing countries. It can be seen as a complex multi-criteria group decision-making (MCGDM) problem that requires the consideration of multiple conflicting tangible and intangible criteria. Nevertheless, few academics have been concerned about this issue. Moreover, current MCGDM methods have limited support for CMW recycling channel evaluation and they do not consider hospitals’ reverse supply chain strategy when evaluating. Thus, this study presents a novel MCGDM approach based on intuitionistic fuzzy sets (IFSs) and the VIKOR method for assessing the capacity of CWM recycling channels. According to the characteristics of CMW, processing flow and the TOE (Technology, Organization and Environment) theoretical framework, we established a new CMW recycling channel capacity evaluation index system which makes our proposed method more targeted and efficient. In the decision-making process, we integrate the best-worst method (BWM) and entropy to determine the decision makers (DMs) weighting in a more comprehensive way, considering both subjective and objective criteria, which was ignored by many MCGDM methods. A new aggregation operator called IFWA is proposed by us, considering the priority of DMs. Based on both the ranking of capacity and disposal charges, we then position the alternatives in the recycling channel priority index (RCPI) matrix constructed by us. According to this PCPI matrix and the reverse supply chain strategy of hospitals, a more reasonable CMW allocation strategy is determined and a more efficient CMW reverse supply chain is designed. Finally, a real case study from Wuhan was examined to illustrate the validation of our approach.

Single and Interval-Valued Hybrid Enthalpy Fuzzy Sets and a TOPSIS Approach for Multicriteria Group Decision Making

The concept of entropy is one of the most important notions of the information theory. Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process. Shannon’s entropy is one of the most useful entropy types. The notion of enthalpy is the information energy expressed by the complement of Shannon’s entropy. In this paper, we propose the concept of interval-valued hybrid enthalpy fuzzy set by modifying single and interval-valued fuzzy multisets. In this context, an interval-valued hybrid enthalpy fuzzy set contains information about both the data and their entropy. We also provide a cosine similarity measure between interval-valued hybrid enthalpy fuzzy sets. Using this cosine similarity measure, we propose a TOPSIS approach for multicriteria group decision making. Moreover, we apply the proposed TOPSIS method to a research assistant selection problem, and we compare the result with the result of a classical TOPSIS method.

A Risk Assessment Model for Cyber-Physical Water and Wastewater Systems: Towards Sustainable Development

Cyber-physical systems (CPS) and their Supervisory Control and Data Acquisition (SCADA) have attracted great interest for automatic management of industrial infrastructures, such as water and wastewater systems. A range of technologies can be employed for wastewater treatment CPS to manage risks and protect the infrastructures of water systems and their wastewater against cyberattacks. In this paper, we develop a novel risk assessment framework, named RAF-CPWS, which perfectly estimates the risks of water and wastewater technologies. To do this, a multi-criteria group decision-making (MCGDM) approach is designed by neutrosophic theory to assess the risks of wastewater treatment technologies (WWTTs). The proposed approach evaluates the best WWTTs, considering various economic, environmental, technological and cybersecurity, and social factors. A decision-making trial and evaluation laboratory (DEMATEL) is employed to evaluate the significance of the adopted factors in a real testbed setting. The proposed approach contributes to a comprehensive measure of WWTTs through several factors, revealing its high sustainability and security in assessing the risks of cyber-physical water and wastewater systems.

Consensus reaching process in large-scale group decision making based on bounded confidence and social network

Consensus reaching process (CRP) is a dynamic and interactive method used to reach a group decision. Now that social networks and mobile internet are prominent features in daily life, more experts are able to take participate in decision making in the network and their opinions are influenced by others in the decision-making process. Therefore, how to use the difference of opinions and the relationships between experts to promote the consensus is an important issue. This paper proposed a CRP approach for large-scale group decision-making based on bounded confidence and social network to manage experts’ opinions. Firstly, a fast unfolding algorithm was used to reduce the dimension of the large-scale and the experts’ weights were obtained by social network analysis. Secondly, the CRP was built based on the Manhattan distance, and the feedback mechanism was developed to adjust experts’ opinions based on bounded confidence and social network when the experts did not reach a consensus. A numerical example was used to show the feasibility of the proposed approach and the results illustrated that the consensus speed was faster and the information managed more efficiently. The comparisons with other approaches showed the advantage of our proposed approach. Finally, simulation experiments were given to verify the effectiveness of our proposed approach.

A Grey Incidence Based Group Decision-Making Approach and Its Application

We define the group measure matrix of the alternative scheme and the ideal scheme based on the relevant factor sequence and the system characteristic behavior sequence. Furthermore, the information distances of decision makers and decision criteria are defined, respectively. According to the information distance, we obtain each scheme’s grey matrix incidence degree for the scheme ranking. Finally, we use an example to verify the rationality of the model and compare it with other classic methods, such as TOPSIS, VIKOR, MULTI-MOORA. Compared with previous grey incidence analysis model, the proposed model can make full use of information of the decision-maker dimension and the criteria dimension. The proposed model can avoid high-dimensional information loss. The results show that the proposed method has superiority in measuring decision-maker information and decision-making standard information.

A multivariate minimum cost consensus approach for two-level group decision making

A multivariate minimum cost consensus approach for two-level group decision making

Matrix approach for fuzzy description reduction and group decision-making with fuzzy [formula omitted]-covering

Fuzzy β-minimal description (β-FMiD) and fuzzy β-maximal description (β-FMaD) are two basic information granules. Their uses include eliminating redundant information when describing an object contained in this information and inducing different types of fuzzy β-neighborhoods. Currently, various operators (such as approximation operators, positive domain operators, and dependency functions) induced by fuzzy β-neighborhoods are commonly used to deal with attribute reduction problems. Essentially, β-FMiD and β-FMaD are the basis of the existing methods. Hence, it is necessary to use β-FMiD and β-FMaD directly to solve attribute reduction, which will simplify the calculations and improve the redundancy rate. However, it is time-consuming and error-prone when employing the existing methods to calculate β-FMiD and β-FMaD. To solve this problem, novel matrix approaches of β-FMiD and β-FMaD are proposed, which are applied to two categories of reductions and to the issue of multi-criteria group decision-making (MCGDM) in this research. One approach is the correction and improvement of the existing method; the other is a novel method by using the matrix representation of fuzzy β-covering directly and the binary relation of inclusion between elements in the fuzzy β-covering. In addition, four classifications of fuzzy β-neighborhood operators are calculated by the matrix representations of β-FMiD and β-FMaD. Two new categories of reductions of fuzzy β-covering information systems (β-FcISs) are also proposed via β-FMiD and β-FMaD, respectively. These two types of reductions are calculated via matrices and applied to the problem of MCGDM to eliminate fuzzy granular structures, which are not needed to reach a decision in MCGDM. Finally, several experimental comparisons are posited to show the computational efficiency of the proposed approaches on nine UCI datasets. Experimental results demonstrate that the suggested methods are promising and comparable to other designs.

Complex Spherical Fuzzy Decision Support System Based on Entropy Measure and Power Operator

The objectives of this paper are to define novel aggregation operators (AOs) for aggregating different complex spherical fuzzy numbers (CSFNs) under the influence of their membership grades. The uncertainties included in the information are dealt with in contemporary studies of the fuzzy set and its extensions by membership grades, which are a subset of real numbers that lose some relevant information and hence alter the decision results. The conversion to these complex spherical fuzzy sets addresses the classes’ uncertainty, whose ranges differ from the specific subset of the complex subset of the unit disk. For this purpose, we defined new CSF power AOs. Some of the desirable properties of these operators have also been investigated. A multiattribute group decision-making (MAGDM) approach is implemented in the structure developed by the CSFNs on the basis of these operators. A numerical example concerning the selection of the best alternatives is given to demonstrate the effectiveness of the defined method and is tested by comparing its results with the various methods.

Clustering and compatibility-based approach for large-scale group decision making with hesitant fuzzy linguistic preference relations: An application in e-waste recycling

Considering that large-scale group decision making (LSGDM) has been widely concentrated on clustering and lower compatibility problems, this paper focuses on clustering and consensus reaching of hesitant fuzzy linguistic preference relations (HFLPRs) in LSGDM. Firstly, we define the hesitant degree and the fuzzy degree functions for hesitant fuzzy linguistic term sets (HFLTSs) according to their scale and deviation of elements. To normalize HFLTSs with different length, a minimum deviation model based on the hesitant and the fuzzy degrees is developed. After that, we propose a new similarity measure and compatibility measure via integrating the hesitant and fuzzy degrees. Then a clustering process combining similarity and compatibility is used to assign large-scale experts into several clusters. The clustering method not only strengths the cohesion of subgroups, but also guarantees that HFLPRs in subgroups are acceptable compatibility. Moreover, a compatibility adjusting process with self-selected feedback mechanism is presented to improve the compatibility level of clusters until they reach a predefined threshold in selection process. And we combine the number of members in subgroups and the compatibility level of subgroups to determine weights of subgroups. Finally, a case of e-waste recycling and comparison are analyzed to show the availability of the proposed method.