Group Decision-making Process Research Articles

Adaptive Formation by Pedestrian Small Groups During Egresses

Human crowd behavior is an intriguing example of collective behavior where global dynamics emerge from interindividual interactions. Although most studies of human crowds assume that all individuals are independent, the presence and impact of social groups, such as friends or families, have recently been explored. Spatial structures resulting from group behavior and decision-making processes are key aspects of social groups, and how such structures are formed and the impact of these are largely unknown, especially in the scenario of crowd evacuations. Here we experimentally show that in the vicinity of exits, dyads and triads form a representative formation, which would attenuate a disadvantage of small groups that are typically regarded as moving obstacles toward the evacuation dynamics. In laboratory experiments on crowd evacuations regarding small group behavior, we first observed no noticeable differences of egress time between monads and groups, which is contrary to a naïve intuitiveness but in agreement with recent experimental observations. To resolve this contradiction, a detailed analysis focusing on behavior in front of exits suggests that group members constitute a straight-line structure vertical to the exit. Our findings offer a new perspective on how adaptive small group behavior emerges during crowd evacuation.

Stochastic multi-attribute group decision-making for long-term comprehensive operation of cascade reservoirs under multiple uncertainties

Study regionThe Three Gorges cascade (TGC) reservoirs, China. Study focusSince uncertainties inherent in inflow forecasting, elevation-storage, discharge capacity, flood routing, and the preferences of decision makers (DMs) persist in the attribute measurements (AMs) and weights (AWs), problems where multiple DMs select the best compromise scheme for the long-term comprehensive operation of cascade reservoirs considering numerous attributes become a stochastic multi-attribute group decision-making (MAGDM) process. To represent uncertain AMs and AWs and consider the bounded rationality of DMs, we propose a stochastic MAGDM with integrated stochastic multicriteria acceptability analysis (SMAA) and gray cumulative prospect theory (GCPT), namely, SMAA-GCPT. After quantifying the uncertainties, SMAA-GCPT is used to model uncertain AMs and imperfect AWs with calculable probability distributions and map the sampled AMs and AWs to utility values to reflect the overall performance assuming bounded rationality. New hydrological insights for the regionExperiments on the TGC indicate that SMAA-GCPT can effectively differentiate alternative schemes and select the best compromise scheme to harmonize the conflicting benefits and risks of the TGC, with higher reliability than SMAA-2 and SMAA-GRA. The influence of uncertain AMs on the reliability of SMAA-GCPT is more obvious than that of uncertain AWs. The decision-making error risk is primarily sensitive to the inflow forecasting uncertainty, followed by the flood routing uncertainty, and is less affected by the uncertainties in elevation-storage and discharge capacity curves.

Consistency and consensus checking and improving methods for group decision-making with hesitant fuzzy preference relations

Decision-makers (DMs) are often hesitant about the evaluations owing to objective or subjective reasons, and determined preference relations always have various limitations on the way that DMs express their evaluations. To address this, this study investigates the group decision-making (GDM) process with hesitant fuzzy preference relations (HFPRs), and develops the methods of consistency and consensus checking and improving processes. To do this, the definitions of best consistency index (BCI) and worst consistency index (WCI) are given. On account of this, the concept of acceptable additive consistent HFPR is proposed. Moreover, the methods of consistency and consensus measurement and improvement processes are introduced for GDM with HFPRs, which can automatically obtain effective and scientific consistency and consensus results. Furthermore, a priority method for HFPRs is employed to rank the alternatives. The procedure of the GDM problem with HFPRs is introduced in detail, and an example of selecting a potential supplier for a solar company is used to demonstrate the proposed method. Comparative analyses shows that the proposed method is more powerful for dealing with the hesitant and uncertain situations and greatly expands the information description scope.

Evaluating the Barriers to Blockchain Adoption in the Energy Sector: A Multicriteria Approach Using the Analytical Hierarchy Process for Group Decision Making

The blockchain has been proposed for use in various applications in the energy field. Although the blockchain has technical strengths, several obstacles affect the application of the technology in energy services. The scope of this study is to highlight and prioritise the most important barriers to such applications. The first step in this direction is specifying the potential areas of the implementation of blockchain technology in the energy sector. Two useful tools for market analysis were used: Political, Economic, Social, Technological, Legal and Environmental, PESTLE Analysis, and Strengths, Weaknesses, Opportunities and Threats, SWOT Analysis, which examine external and internal factors, respectively. Thus, a list of the most important elements hindering the incorporation of the blockchain in the energy sector was extracted. The detected barriers were classified and ranked by energy and IT experts using the multicriteria method, “Analytical Hierarchy Process for Group Decision Making”. The results reveal that legal barriers relating to the complexities of deficiencies of regulations are the most significant, while technological barriers, especially those related to security issues, are also important. Sociopolitical barriers related mainly to lack of trust in blockchain, as well as economic concerns such as high upfront costs, are less influential but should still be considered. The conclusions of the conducted research have the potential to guide market actors in their endeavours to modernise energy systems through the use of the blockchain, assisting them in designing the most appropriate market strategies.

Interaction and risk preference-based group decision-making with intuitionistic fuzzy preference relations

This research proposes a new method to solve group decision-making(GDM) problems with intuitionistic fuzzy preference relations(IFPRs). First, a new definition of multiplicative consistency of IFPR is presented to address the defects of the existing consistency definitions. Then, two programming models are established to obtain the most optimistic and pessimistic consistent IFPRs and corresponding intuitionistic fuzzy priority weights. Also, in order to improve the accuracy of aggregate information, a new method to determine the weights of decision-makers(DMs) is offered by considering the interaction among DMs. Subsequently, by combining the vagueness and non-vagueness of the aggregated information, a multiplicative consistency definition of the collective IFPR is provided. Moreover, to simplify the GDM process, a programming model for solving the priority weight is established, which effectively avoids the consistency test and correction of IFPRs. Finally, the values of the proposed method are illustrated by comparative analysis.

An improved WINGS-ISM approach with consensus model in group decision-making for addressing critical success factors of collaborative emergency management

Since the variety and destructiveness of unexpected natural disasters and health emergencies, and the weakness of emergency systems around the world, studies on collaborative emergency management (CEM) are receiving increasingly attention recently. The existing literature has proved that with respect to the constraint of limited resources, it’s usually unrealistic for CEM practitioners to push forward all influential factors that affect the comprehensive performance of CEM simultaneously. Thus, identifying the most critical success factors (CSFs) is of great significance to promote the effectiveness of CEM practices. To achieve this intention, we proposed a novel multi-granularity extended probabilistic linguistic weighted influence nonlinear gauge system and interpretative structural modeling (WINGS-ISM) approach with a consensus model optimized by the convolutional neural network algorithm. The new technique specializes in addressing the CSFs of episodes involving CEM characterized by the combination of uncertainty and complexity in group decision-making process. To validate its feasibility and robustness, we mainly investigate 10 CSFs to conduct an illustrative CEM case, the result shows that ‘Applicable emergency response plan and regulations’, ‘Government unity of leadership and coordination’ and ‘Reasonable organizational structure and clear functions’ are identified out of 10 influential factors. Besides, the cause-effect and hierarchal relationship digraphs indicate that the CSFs of CEM are interconnected, and this finding can support CEM practitioners to devote more attention to the most important factors, which may yield more improvements in CEM practices. Finally, some research implications are provided for better improving the effectiveness of CEM practices based on the findings of this study.

A Confidence and Conflict-based Consensus Reaching Process for Large-scale Group Decision Making Problems with Intuitionistic Fuzzy Representations

A Confidence and Conflict-based Consensus Reaching Process for Large-scale Group Decision Making Problems with Intuitionistic Fuzzy Representations

A Survey of Fuzzy Best-Worst Group DecisionMaking Process towards Human Centricity

A Survey of Fuzzy Best-Worst Group DecisionMaking Process towards Human Centricity

GRUP KARARLARINDA AZINLIK ETKİSİ KAVRAMININ “12 KIZGIN ADAM” FİLMİ ÜZERİNDEN İNCELENMESİ

The aim of this study is to examine the group dynamics and decision-making process in the movie "12 Angry Men" with a micro-level critical discourse analysis. The study aims to explore the influence of a member within the group on collective decisions and the influence of the first advocate of face-to-face group discussion. The leadership behavior of Henry, the main character of the movie, influences the group despite being in a minority position, and provides a platform for discussion. By tackling different topics, Henry creates differences of opinion among the majority, thereby understanding the thinking of each member and shaping their speech. Henry's position in the group can be explained by his minority influence; because in the beginning only he did not comply with the majority by voting "not guilty". However, he later acted consistently in his decision, causing the group's thoughts and votes to change. This study provides an important example for understanding the complexity of group dynamics and communication and highlights the role of leadership in minority influence.

A hybrid generalized TODIM approach for sustainable 3PRLP selection in electronic manufacturing industry

Under the pressure of globalization, environmental protection/resource consumption, and the intensification of market competition, various industries have recognized the significant importance of sustainable supply chain management (SCM), in which reverse logistics (RL) has attracted many concentrations from governments to enterprises and the public. However, it is relatively difficult to operate RL autonomously for most electronic manufacturing companies whose core businesses and competition advantages are not in logistics operations because of limited resources and professional skills. Therefore, it is a good choice to escape from the predicament by selecting an appropriate third-party reverse logistics provider (3PRLP) with professional skills for outsourcing and accomplishing the RL business. Generally, evaluation and selection of a 3PRLP is regarded as a multi-attribute group decision-making (MAGDM) process, in which four aspects, namely, the evaluation attributes system, weight of experts and attributes, preference information representation and alternative ranking method have impacts on the decision-making results. Therefore, this paper constructs an MAGDM framework named generalized TODIM (TOmada de DecisãoInterativa e Multicritério, Portuguese acronym for Interactive multicriteria decision-making) under a hybrid information environment, which includes crisp numbers, interval numbers and fuzzy linguistic terms, for solving the problem. It provides a useful reference for manufacturing enterprises to find out the optimal 3PRLP from many alternatives. In particular, the use of different information forms for different evaluation attributes can not only facilitate decision makers to provide their judgments, but also effectively retain the original decision information as much as possible. Utilization of the generalized TODIM method is helpful for considering the risk attitudes and psychological preference behaviors of decision-makers. Finally, to demonstrate the feasibility and effectiveness of the proposed method, a case study relates to select a sustainable 3PRLP in an electronic manufacturing company was conducted. The findings show that the company should pay more attention to the attributes related to social responsibility, service quality and cost when selecting alternative 3PRLP under the sustainable SCM environment. It also provides useful suggestions for 3PRLP themselves to improve their competitiveness from these aspects. Moreover, the sensitivity and comparative analysis demonstrated the necessity and superiorities of comprehensively considering weights of experts and attributes, adopting hybrid information formats, and taking into account the risk attitudes and psychological preference behaviors of decision-makers during the evaluation and selection process.

Some T-spherical fuzzy dombi hamy mean operators and their applications to multi-criteria group decision-making process

Renewable energy sources play an influential role in the world’s climate and reduce the rate of harmful gasses such as carbon dioxide, methane, nitrous oxide, and many other greenhouse gasses that contribute to global warming. The theoretical concept of the T-spherical fuzzy (T-SF) set (T-SFS) is the most suitable model to evaluate energy resources under uncertainty. This article illustrates appropriate operations based on Dombi triangular norm and t-conorm. We derived a series of new aggregation approaches, such as T-SF Dombi Hamy mean (T-SFDHM) and T-SF weighted Dombi Hamy Mean (T-SFDWHM) operators. Further authors illustrated a list of new approaches such as T-SF Dual Dombi Hamy mean (T-SFDDHM), and T-SF Dombi weighted Dual Hamy mean (T-SFDWDHM) operators. Some exceptional cases and desirable properties of our derived approaches are also studied. We illustrate an application of renewable energy resources to be evaluated using a multi-attribute group decision-making (MAGDM) method. A case study was also studied to choose appropriate energy resources using our proposed methodology of the T-SFDWHM and T-SFDWDHM operators. To show the effectiveness and validity of our current methods, we compared the existing results with currently developed aggregation operators (AOs).

An interval-valued intuitionistic fuzzy group decision-making method for evaluating online knowledge payment products

Increased demand for knowledge, evolving consumer attitudes, and the convenient online transaction opportunities provided by Internet technology have led to the rapid rise and growth of online knowledge payment. At present, studies on the online knowledge payment industry mainly focus on exploring the influencing factors of users' willingness to pay for online knowledge payment products (OKPPs), and there are fewer studies on the evaluation and rating methods of OKPPs. To address this problem, this paper proposes an improved group decision-making method based on consensus adjustment and prospect theory to realize the evaluation of OKPPs from the perspective of consumer experience value. The method uses interval-valued intuitionistic fuzzy numbers to process evaluation information, selects leading users (LUs) as decision makers, and identifies four evaluation criteria based on consumer experience value, which are functional value, self-fulfillment value, hedonic value, and emotional value. The proposed group decision-making method in this paper takes into account the consensus problem of LUs’ opinions, proposes a consensus adjustment method, and uses prospect theory to incorporate the psychological factors of LUs into the group decision-making process. Finally, the effectiveness and advantages of the method proposed in this paper are verified using an example and a comparison with existing methods. This research will provide methodological reference for knowledge payment platforms (KPPs) to select high quality OKPPs, and will also urge knowledge producers (KPs) to create OKPPs that can bring higher experience value to knowledge consumers (KCs). At the same time, this research makes possible the co-creation of knowledge between KPs and KCs, and enriches and develops the relevant research on the online knowledge payment industry.

Green Supplier Selection Based on Sequential Group Three-Way Decision Making

Environmental protection and sustainable development have become the consensus of all countries in the world. Enterprises must pay attention to the impact on the environment in their operations. Therefore, the selection of green suppliers has become a crucial issue for companies. Supplier selection is a dynamic and complex multi-attribute group decision-making process. The decision results have the tripartite characteristics of “accepted”, “rejected” and “pending further investigation”, and experts need to constantly negotiate in the decision-making process to achieve consensus. In view of the above characteristics, this study constructs a sequential group three-way decision making (TWDM) method to support green supplier selection. Firstly, we review the existing literature on the evaluation criteria and selection methods of green suppliers. In this process, we construct an evaluation attribution system including the following four aspects: product formation, service level, development capability, and green level. Secondly, combining with the sequential and group characteristics of decision making, we propose a multiple-attribute sequential group TWDM method based on a multi-level granularity structure. The weight of each decision maker is determined by his influence weight and interval-valued intuitionistic fuzzy entropy weight. The attribute weight is determined by entropy weight and subjective weight. By using the VIsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method improved by grey relation analysis (GRA), we calculate conditional probabilities. Since the cumulative prospect theory can reflect the risk attitude of decision makers, we apply it to calculate the decision thresholds. Finally, we apply a case of Q automobile manufacturer to verify the effectiveness, applicability and feasibility of the method. The innovation of this study is to construct social networks at each granularity level and introduce an expert information exchange model to promote group consensus. The sequential group TWDM method provides a new reference and idea for the selection of green suppliers.

Ordered weighted utility distance operators and their applications in group decision-making

In multiple attribute group decision-making (MAGDM), a consensus exists that the psychological factors of decision makers (DMs) generally influence their choice of optimal alternatives. To quantify well the influence of DMs’ risk attitudes on the group decision-making process, this paper develops a new approach to MAGDM by introducing DMs’ utility functions into the aggregation process. First, the ordered weighted utility distance (OWUD) operator is constructed by introducing DMs’ utility functions into the distance operator. As a novel operator, the OWUD operator not only satisfies both the basic features of distance measures and the desirable properties of aggregation operators, but also has some unique characteristics. Then, the linear risk tolerance (LRT) utility function is further used in the OWUD operator as the basic utility function, and a specific form of the OWUD operator, which is named as the ordered weighted-linear risk tolerance utility distance (OW-LRTUD) operator, is proposed. The OW-LRTUD operator can quantify well the influence of DMs’ risk attitudes on the group decision-making process. It also has an analytically tractable form and includes many well-known distance measures and aggregation operators, and thus can be applied to many different kinds of decision-making problems. Subsequently, to determine the weights of the OW-LRTUD operator, an extension of the maximum Bayesian entropy method is proposed by incorporating the prior known weight information and different risk attitudes of all individual DMs. An approach to MAGDM based on the OW-LRTUD operator and the proposed weight determination method is developed. An application for determining the optimal alternative in the investment selection is provided to show the feasibility, effectiveness and robustness of our approach in practice, and the corresponding sensitive analysis illustrates the influence of DMs’ risk attitudes on the group decision-making process.

Decision making among residents in training of obstetrics and gynecology: A qualitative exploration in Pakistani context.

Medical decision-making is critical and the decisions are made under uncertain, complex, and dynamic conditions. In this regard, practitioners' experiences and perceptions may provide a bottom-up knowledge of the issues, as well as a corresponding support system that assists them in learning to make decisions in critical situations. The current study aimed to examine these experiences in the Pakistani context. We interviewed 14 trainee residents (aged 26 to 34 years) from tertiary care hospitals. Using inductive thematic analysis, we explored a participant-centered perspective on the support system and decision-making process. Findings reveal that the major challenges to decision-making include uncertain and complex situations, hospital-related constraints, and sociocultural context. Both non-critical and critical case management are used in individual and group decision-making processes. The residents use knowledge-based, emotional, and instrumental support to make decisions. The study gave practitioners and academics a transdisciplinary platform to explore the cognitive, social, and behavioral aspects of decision-making in the healthcare industry.

An Integrated Group Decision-Making Framework for the Evaluation of Artificial Intelligence Cloud Platforms Based on Fractional Fuzzy Sets

Due to the rapid development of machine learning and artificial intelligence (AI), the analysis of AI cloud platforms is now a key area of research. Assessing the wide range of frameworks available and choosing the ideal AI cloud providers that may accommodate the demands and resources of a company is mandatory. There are several options, all having their own benefits and limitations. The evaluation of artificial intelligence cloud platforms is a multiple criteria group decision-making (MCGDM) process. This article establishes a collection of Einstein geometric aggregation operators (AoPs) and a novel Fractional Fuzzy VIKOR and Fractional Fuzzy Extended TOPSIS based on the entropy weight of criteria in fractional fuzzy sets (FFSs) for this scenario. The FFSs provide an evaluation circumstance containing more information, which makes the final decision-making results more accurate. Finally, this framework is then implemented in a computational case study for the evaluation of artificial intelligence cloud platforms and comparison of this model with other existing approaches, such as the extended GRA approach, to check the consistency and accuracy of the proposed technique. The most optimal artificial intelligence cloud platform is I1

EVALUATING THE SUCCESS FACTORS OF INTEGRATING THE RAILWAY INTO THE PUBLIC URBAN–SUBURBAN TRANSPORT

The success of integrating the railway into the public urban and suburban transport depends upon a range of internal and external factors, with challenges arising in their number and complex interdependence. Furthermore, the significance and impact of some factors are uneven in cities. There is a necessity for the right mix of different factors that drive significant use of railway into the Public Transport System (PTS). The research problem is finding and defining factors needed to assess the success of railway implementation into public urban and suburban transport. An efficiency analysis of the factors that impact the level of the planned transport system use was conducted in a hierarchical or network system. The 1st step of research is to the determination of relevant factors. In this research 27 factors were determined. Based on the determining factors and their connection it is possible to design an Analytic Network Process (ANP) to evaluate the importance of factors. Factors are divided into 4 clusters: (1) user features, (2) road transport features, (3) services features, and (4) city and corridor features. Testing was carried out by group decision-making process by members of an expert panel. According to the testing, the most important factors according to their weight are: (1) frequency with 0.11651, (2) employment level with 0.10146, (3) ticket price with 0.09714, (4) reliability with 0.07256, and (5) integration of services with 0,07065. Using the weight of each factor it is possible to evaluate the success likelihood of a concrete project. In this research case study was oriented on integrating the railway into the public urban and suburban transport in the City of Rijeka in Croatia. For this purpose, the relevance of the determined weight factors was used for the concrete project of integration in the City of Rijeka that resulting in 67.17% fulfilment of the successful integration railway into the public urban and suburban transport.

An entropy-based group decision-making approach for software quality evaluation

Software quality evaluation (SQE) is a multi-dimensional decision-making problem. The increasingly complex socioeconomic environment has made group decision making (GDM) more important. Determination of the relative contribution (weight) of individual decision makers is an important part of the GDM process. Calculation of the similarity between decision matrices, potentially using a comprehensive projection measure, is also critical. This paper presents: (1) a new entropy-based approach to determine the weight of decision makers; (2) a novel normalized projection approach to measuring the similarity between two decision matrices; and (3) new group regret and satisfaction reference matrices that are modeled in a GDM setting. A comprehensive GDM method is proposed and adopted for SQE. An experimental analysis is conducted to investigate the practicability, feasibility, and validity of the proposed GDM method. The experimental results confirm the proposed GDM method as practicable, producing stable rankings of alternatives, with the same ranking of alternatives being produced in almost all of the dynamic experimental cases.

Multi-attribute decision-making based on data mining under a dynamic hybrid trust network

The issues arising from the evolution of big data and network environment have rendered the relationship among experts to no longer be an independent multi-attribute group decision-making process. In response, a PROMETHEE multi-attribute decision-making method based on data mining under dynamic hybrid trust network is proposed. This method incorporates evaluation similarity based on degree centrality and expert trust values based on K-hop centrality in the social network to determine the experts’ weights. Furthermore, because the expert weight is somewhat affected by the attribute weight, the public-level attribute weight is obtained through data crawling and TF-IDF technology. The comprehensive attribute weight is acquired through a combination of expert-level attribute weights. Additionally, establishing a minimum adjustment cost model enables experts to follow a dynamic consensus reaching process in a composed hybrid trust network. Finally, to express the evaluation information more accurately in a complex linguistic environment, the probabilistic linguistic PROMETHEE method is introduced and applied to the site selection evaluation of charging and swapping stations. This highlights the feasibility and effectiveness of the proposed method through the comparison of decision-making methods and parameter sensitivity analysis.

An Edge-Based Multi-Technology Architecture Healthcare Industry 4.0

Healthcare Industry 4.0 refers to intelligent operation processes in the medical sector. With the development of information technology, large-scale group decision making (GDM), which enables a greater number of decision makers (DMs) from various location or sectors to participate in decision making, has been quickly developed and been applied in Healthcare Industry 4.0 to aid in make decisions effectively and strategically. This study examined numerous actual examples of AI applications in healthcare in addition to doing a thorough analysis of the literature. The findings show that large hospitals are currently utilizing AI-enabled systems to support medical staff in patient diagnostic and treatment activities for a variety of disorders. Additionally, AI technologies are enhancing the effectiveness of administrative and healthcare tasks. Healthcare professionals are welcoming AI, but its uses can be seen from both utopian and dystopian perspectives. In order to generate a thorough understanding of GDM in Healthcare Industry 4.0 and to identify potential future development directions, this study conducts an overview. According to the paradigm of the general GDM process, which involves information representation, dimension reduction, establishing consensus, and result elicitation, the implementations of GDM methods in Healthcare Industry 4.0 are evaluated. We also discuss present research obstacles and potential future developments in relation to medicinal GDM. Our work is believed to be useful for researchers looking into GDM in Healthcare Industry 4.0. Also, discuss the details of those opportunities and challenges to provide a balanced view of the value of AI applications in healthcare. It is clear that rapid advances of AI and related technologies will help care providers create new value for their patients and improve the efficiency of their operational processes.