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The semiconductor industry is a resource-intensive sector that heavily relies on energy, water, chemicals, and raw materials. Within the semiconductor manufacturing process, the diffusion furnace, ion implantation machine, and plasma etching machine exhibit high energy demands or operate at extremely high temperatures, resulting in significant electricity consumption, which is usually carbon-intensive. To address energy conservation concerns, the industry adopts batch production technology, which allows for the simultaneous processing of multiple products. The energy-efficient parallel batch scheduling problem arises from the need to optimise product grouping and sequencing. In contrast to existing heuristics, meta-heuristics, and exact algorithms, this paper introduces the Deep Q-Network (DQN) algorithm as a novel approach to address the proposed problem. The DQN algorithm is built upon the agent's systematic learning of scheduling rules, thereby enabling it to offer guidance for online decision-making regarding the grouping and sequencing of products. The efficacy of the algorithm is substantiated through extensive computational experiments. [ABSTRACT FROM AUTHOR]

Human resource (HR) managers hire conscientious employees because they are both productive and are viewed as upholding high ethical standards due to their propensity to engage in voice. Organizations may strive to create a work context conducive to all employees acting ethically, not just conscientious ones, by centralizing decision‐making authority and promoting formalization through a higher hierarchy of authority. Yet, we propose that from the social information processing perspective, in higher hierarchy of authority contexts, peers may view their highly conscientious colleagues as less ethical. We hypothesize these effects through the lens of trait activation theory, according to which in a higher hierarchy of authority context, others are less likely to notice the voice behaviors of conscientious employees. Problematically, when others fail to notice conscientious employees' voice, they may perceive these workers as being less ethical. We tested our hypothesized moderated mediation model in a matched sample of employees (N = 820), their supervisors (N = 445), and peers (N = 529). As predicted, hierarchy of authority moderated the positive relationship between conscientiousness and voice, which in turn explained others' perceptions of their ethicality. Conscientiousness was positively related to peer assessments of ethicality via promotive (not prohibitive) voice when hierarchy of authority was lower (but not higher), partially supporting our hypotheses. These results suggest HR practitioners should be cognizant of the differential evaluations of highly conscientious employees in contexts with different levels of hierarchy of authority, and continuing challenges associated with balancing flexibility and formalization. [ABSTRACT FROM AUTHOR]

The circular economy (CE) seeks to maintain products and materials at their highest utility and value. The organisational and governmental policy have seised onto the CE philosophy to advance socio-economic and environmental development. CE remains an essentially contested concept – making its utilisation as a foundation for managerial and policy decisions challenging. Circularity assessment has not been systematically adopted, especially within supply chain management. Using critical scholarly and practical evidential foundation, we proposed a comprehensive set of metrics that can be utilised in supplier selection, monitoring, and development for circularity. These metrics include the macro, meso, and micro levels. A group decision-making method integrating best-worst method (BWM), regret theory (RT), and dual hesitant fuzzy sets (DHFS) for circular economy and circularity (CEC) supplier evaluation and selection is introduced – providing instrumental value for the identified metrics typology. The proposed BWM-DHFE-RT integrative analytical method can accommodate decisionmaker psychological behaviour under uncertainty while simultaneously capturing divergent or conflicting opinions of different decision-makers. An illustrative business scenario is utilised to demonstrate the application of the proposed method. Though the proposed CE performance metrics and methodology are used for CEC supplier management reasons they have broader applicability. Future research and application directions are discussed. [ABSTRACT FROM AUTHOR]

Although teams of negotiators are widely assumed to be better at unlocking integrative solutions compared to individual negotiators, the interteam negotiation context is characterized by unique challenges that can make effective collaboration between teams difficult. We extend our theoretical understanding of interteam negotiations by offering novel insights about when and why teams realize their potential in integrative negotiations. Specifically, we propose a theoretical model that explains how hierarchical team structures reduce information elaboration within teams, which reinforces "fixed-pie" assumptions that prompt the reliance on value-claiming behaviors between teams and lower high-quality outcomes such as the joint gain achieved. Across four studies, each involving interactive team-on-team negotiations, we provide support for the hypothesized effects of formal intrateam hierarchies on joint gain, and test a useful intervention to mitigate the harmful effects of hierarchically structured teams at the negotiation table. Contributions to the literatures on team negotiations, interteam collaboration, and hierarchical differences within teams are discussed. [ABSTRACT FROM AUTHOR]

As a generalization of fuzzy sets and intuitionistic fuzzy sets, neutrosophic sets, and their combinations have been developed to represent uncertain, imprecise, incomplete, and inconsistent information existing in the real world. A neutrosophic set has the potential to be a general framework for uncertainty analysis in various spectra. On the other hand, the idea of Fermatean neutrosophic sets (FNSs) is the hybrid model of Fermatean fuzzy sets and neutrosophic sets, developed to enable the analytical management of ambiguous data from relatively typical real-world decision-making scenarios. In this work, we develop an algorithm to compare the Fermatean neutrosophic set and the neutrosophic set, Fermatean Neutrosophic Weighted Arithmetic Mean (FNWAM) and Fermatean Neutrosophic Weighted Geometric Mean (FNWGM) operators as an accuracy function in addition to the conventional aggregating operators are designed. Further, the CODAS technique for Multiple Attribute Group Decision Making (MAGDM) problems according to the defined operators is proposed. Additionally, to make a clear understanding of the presented study, a university faculty selection problem is studied to illustrate the proposed methodology. Finally, the results of the above two sets prove the authenticity of this study. [ABSTRACT FROM AUTHOR]

In the process of multi-attribute group decision-making (MAGDM), the cubic q-rung orthopair fuzzy sets (Cuq-ROFSs) are utilized to express membership and non-membership degrees in the form of interval values to efficiently cope with decision makers' (DMs') complex assessment values. To more efficiently capture DM evaluation results in the MAGDM procedure, we offer a novel tool called 2-tuple linguistic cubic q-rung orthopair fuzzy set (2TLCuq-ROFS), which extends Cuq-ROFS by using 2-tuple linguistic (2TL) terms. 2TLCuq-ROFS effectively incorporates the advantages of 2TL and Cuq-ROFS, making them attractive and versatile for depicting attribute values in an uncertain and complex decision-making environment. To effectively aggregate the attribute values in the form of 2-tuple linguistic cubic q-rung orthopair fuzzy numbers (2TLCuq-ROFNs), some Maclaurin symmetric mean (MSM) operators and their weighted forms are presented in this paper. The weight information for attributes is unknown. Therefore, the criteria importance through inter-criteria correlation (CRITIC) method is employed to determine the objective weight information. The purpose of this study is to incorporate a conventional multi-attributive border approximation area comparison (MABAC) framework based on 2TLCuq-ROFNs because it addresses problematic and imprecise decision-making problems by calculating the distance among each alternative and the border approximation area by using 2TLCuq-ROFNs and MSM aggregation operators. First, some basic concepts associated with 2TLCuq-ROFNs and the CRITIC-MABAC procedure are briefly explained. Moreover, an evaluation framework based on the improved CRITIC-MABAC method is established. An explanatory case study related to the risk investment problem in Belt and Road is used to verify the validity and practicality of the designed evaluation framework. In conclusion, by utilizing the CRITIC-MABAC methodology based on proposed operators, we find that Λ 7 is the optimal alternative for risk investment. Furthermore, comparison analysis emphasizes the integrity and prominent features of the proposed methodology and provides various complementary perspectives for investors. [ABSTRACT FROM AUTHOR]

In real-world complex group decision-making problems, preference inconsistency and opinion conflict are common and crucial challenges that need to be tackled. To promote consensus reaching, a novel group consensus reaching model is constructed considering individual satisfaction and group fairness. This study focuses on managing the group consensus reaching process based on flexible and adaptable information, modelled as distributed linguistic preference relations (DLPRs). First, a building process for DLPRs is discussed by integrating cumulative distribution functions converted from single linguistic term sets, hesitant fuzzy linguistic term sets, and comparative linguistic expressions. Furthermore, a two-stage consistency improvement method is proposed, which makes a trade-off between the frequency and magnitude of adjustments. Finally, we establish an improved group consensus model to balance individual satisfaction and group fairness, where individual satisfaction is measured by the deviation between the original and revised preferences and group fairness is measured by the deviation between individual satisfactions. The emergency response plan selection is conducted to show the validity and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

The study's primary focus is on examining fairness deliberations of bargainers in Germany and in China under symmetric and asymmetric bargaining power to understand whether they incorporate fairness into their decision process and, if so, how this affects bargaining outcomes. To this end, we conducted an incentivized ultimatum bargaining experiment with symmetric and asymmetric outside options. Groups (N = 142) of three persons interact as proposers and responders in dyads and decide simultaneously on their offer or which offers to accept or reject. Communication between parties is inhibited. We videotaped in-group discussions; the resulting transcripts were text analyzed by eliciting whether groups make fairness an issue, which fairness norms they discuss, and whether they use fairness-related perspective-taking to overcome the communication constraint. We find that asymmetry of bargaining power in favor of the proposer leads to lower offers relative to the symmetric situation. Not all groups make fairness an issue, and fairness deliberations alone have no significant impact on offers. However, when associated with the equal-payoff norm, and in Chinese groups in particular, discussing fairness increases offers in symmetric but also in asymmetric situations, in which other fairness norms could have been applied, too. Fairness-related perspective-taking is used by German and Chinese groups and is associated with higher offers in the former. Our study makes an epistemological and related methodological contribution: a possibly biased interpretation of bargaining outcomes can be mitigated if information on decision processes and underlying mechanisms were available. [ABSTRACT FROM AUTHOR]

The objective of this research is to develop some novel operational laws based of T-norm and T-conorm and then using these operational laws to develop several Einstein operators for aggregating the different complex intuitionistic fuzzy numbers (CIFNs) by considering the dependency between the pairs of its membership degrees. In the existing studies of fuzzy and its extensions, the uncertainties present in the data are handled with the help of degrees of membership that are the subset of real numbers, which may also loss some valuable data and hence consequently affect the decision results. A modification to these, complex intuitionistic fuzzy set handles the uncertainties with the degree whose ranges are extended from real subset to the complex subset with unit disk and hence handle the two-dimensional information in a single set. Thus, motivated by this and this paper we present some novel methods such as complex intuitionistic fuzzy Einstein weighted geometric aggregation (CIFEWGA) operator, complex intuitionistic fuzzy Einstein ordered weighted geometric aggregation (CIFEOWGA) operator, complex intuitionistic fuzzy Einstein hybrid geometric aggregation (CIFEHGA) operator, induced complex intuitionistic fuzzy Einstein ordered weighted geometric aggregation (I-CIFEOWGA) operator and induced complex intuitionistic fuzzy Einstein hybrid geometric aggregation (I-CIFEHGA) operator. We present some of their desirable properties such as idempotency, boundedness and monotonicity. Furthermore, based on these methods a multi-attribute group decision-making problem developed under complex intuitionistic fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to show the effectiveness, importance and efficiency of the novel approach. [ABSTRACT FROM AUTHOR]

Artificial intelligence and blockchain can improve the effectiveness of leadership decision-making in two dimensions. Artificial intelligence technology can improve the scientificity of leadership decision-making, and blockchain technology can guarantee the democracy of leadership decision-making. Society pushes everyone to be gregarious. Group recommendation is thus one of the research focuses in recent years. Prior group recommendation algorithms fail to consider either the influence of group structure on computing scale or the impressions users of higher weights leave on other group members. To address the aforementioned challenges, this paper proposes a group recommendation model based on members' influence and leader impact. In this paper, a model has been proposed to compute members' influence on each other based on interactions and presence. The decisions of leaders identified by the proposed model are the basis for further group recommendation, which yields satisfactory recommendations for most group members as leaders' judgments are more professional. Experimental results on real-world datasets demonstrate better accuracy of the proposed method compared to those of the mainstream group recommendation algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a well-established method for measuring efficiency among a comparable group of decision-making units (DMUs). DMUs comprise entities with time-related activities—i.e., inputs and outputs. The concept of DMU is not reserved only for business entities; it can also be a project or product. This study focuses on the latter by using DEA efficiency scores to estimate the product price from suppliers' perspective of newly developed products. These prices are then used as a basis for negotiation. However, DEA-based estimations of such product-related purchasing can only account for deterministic input and output relationships and cannot handle unobservable negotiation behavior. Therefore, we propose a two-stage estimator in which DEA is a deterministic baseline estimator that captures production-related price components. We then train regression trees to estimate the behavioral bargaining surplus. We present a real-world application stemming from the automotive supplier industry to demonstrate the applicability of our approach. Most importantly, we confirm the effectiveness of our approach by substantiating the hypothesis that our method provides better estimates than one-step machine learning methods, especially when there is little knowledge about new products, i.e., when data availability is limited. [ABSTRACT FROM AUTHOR]

This study investigated the myriad risk factors contributing to cost overruns in healthcare construction projects. It determined the impact of managerial, financial, workerpower, and environmental considerations, focusing on the intricacies of healthcare facilities. By employing a modified fuzzy group decision-making approach (MFGDMA), the research methodically quantified the subjective perceptions of diverse stakeholders involved in project execution. Results reveal that labor-related issues—such as shortages, productivity, and skill deficits—along with managerial delays, particularly in decision-making and change orders, significantly contribute to cost escalations. The study's novelty lies in its integration of fuzzy logic to defuzzify risk perceptions, providing a clear hierarchy of risk factors, which assists in the strategic prioritization of risk mitigation efforts. Additionally, it demonstrates a risk response plan considering the fuzzy-based risk assessment findings, which can serve as a hands-on tool guiding healthcare project practitioners to develop their risk management plans and monitor cost overrun risks throughout the execution phases of a project. The application of the research findings suggests policy implications for improved risk management frameworks toward the development of more-resilient and -efficient strategies for managing healthcare construction projects without prohibitive cost overruns. [ABSTRACT FROM AUTHOR]

In linguistic decision theory, multi-attribute group decision-making (MAGDM) of linguistic neutrosophic numbers (LNNs) is one of the crucial research topics. Existing LNN aggregation algorithms do not consider trigonometric periodicity. In this case, they cannot perform MAGDM problems including periodic/multitemporal applications in LNN scenarios. To fill this gap, this article aims to present a MAGDM technique using the LNN tangent Dombi aggregation operators (TDAOs) for addressing MAGDM issues including multitemporal/periodic applications in LNN scenarios. First, we present the linguistic tangent Dombi t-norm and t-conorm and the LNN tangent Dombi operation laws. Second, we propose the LNN tangent Dombi weighted average and geometric operators and investigate their properties. Third, a MAGDM technique is built based on the two presented operators to tackle MAGDM problems in a LNN scenario. Lastly, the built MAGDM technique is applied to a choice of new energy design schemes in Ningbo City, China, and then its efficiency and appropriateness are verified by comparing with the existing MAGDM technique in the LNN scenario. [ABSTRACT FROM AUTHOR]

We present a novel Data Envelopment Analysis (DEA)-type method to evaluate the productive efficiency of Decision Making Units (DMUs) when the empirical analyst has incomplete output information. Our method builds on the Afriat Theorem that was originally proposed in the context of consumer analysis. We translate this result to a production setting and show that it provides a productive basis for cost efficiency analysis in the absence of output information. Our method is versatile in that it can accommodate a continuum of instances characterized by incomplete information on output quantities. We illustrate its practical usefulness through an empirical application that evaluates the productive efficiency performance of countries in producing national welfare. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) models give each decision making unit freedom to assign values to the input-output weights maximising its efficiency score. However, there is no guarantee that the optimal weights obtained from a DEA model are always positive which poses a problem, in both theory and practice. This paper offers new linear programming models to deal with the zero weights problem without restricting the weights or placing prior value judgments on them while maintaining the original DEA frontier. These models generate a profile of weights with the maximum number of positive weights applicable to both envelopment and multiplier DEA settings. These linear programs still allow weight flexibility and are independent of the solver. We show how these models can be modified to provide unit-specific positive weights. We illustrate the relevance of our approach using artificial and real world data. [ABSTRACT FROM AUTHOR]

This article argues that current attempts to demonstrate the plausibility of democratic procedures' tendency to produce good/wise/better outcomes at least partially fail. The examples given, while suggestive of a power in group decision-making, rarely reference anything resembling an actual political problem. The reason the examples do not do the work purveyors of them think they do, is that political problems seem much more complicated than the examples suggest. The author goes on to contend that a more productive strategy is to embrace the complexity of political problems and think through which dimensions of complexity invite democratic and epistemic opportunities. As such, the author closes by suggesting there are three significant dimensions of political problems, and only in some cases do those problems lend themselves to a democratic procedure producing a wiser/better outcome. Epistemic reliability therefore depends on the kind of problem a democratic procedure is attempting to solve. [ABSTRACT FROM AUTHOR]

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

Community Advisory Boards (CABs) often, "carry" important ideas and concepts for the larger organization of which they are a part. The word "carry" in this context, means that a person or group expresses verbal and nonverbal messages that inform others of what the institution acknowledges, and also what it cannot bear to feel or talk about. These expressions may include attitudes and expectations, values, risks, or disowned features and qualities. A group can contain, "carry," and express formal, informal, and unconscious issues for a department or system. In this article, we situate our theoretical underpinning of this carrying function by relying on a number of literatures: identified patient, splitting and projection, parallel process, and container and contained. Specifying and examining the dynamics of what CABs carry for an institution can prevent common pitfalls for these groups, such as mistrust, feelings of disrespect, lack of productivity, and thwarted expectations. [ABSTRACT FROM AUTHOR]

In many aspects of life on earth, individuals may engage in cooperation with others to contribute towards a goal they may share, which can also ensure self-preservation. In evolutionary game theory, the act of cooperation can be modelled as an altruistic act of an individual producing some form of benefit that can be utilised by others they are associated with at some personal cost. In various scenarios, individuals make use of information that they can perceive within a group to aid with their decision-making regarding who they should associate and cooperate with. However, cooperative individuals can be taken advantage of by opportunistic defectors, which can cause significant disruption to the population. We study a model where the decision to establish interactions with potential partners is based on the opportune integration of the individual's private ability to perceive the intentions of others (private information) and the network position (ranking) of potential partners (public information). We find that there are rankings, such as degree and eigenvector, which can lead to a significant increase to the prosperity of the network, but this greatly increases the likelihood of a network succumbing to cheater invasion. Other rankings, such as betweeness, can instead lead to more stable resilient networks whilst also cultivating some degree of payoff. Our results highlight how commonly used network rankings can be utilised to aid with connection formation within networks and in turn can be utilised to improve the well-being of these networks, helping with stability and allowing for individuals to cultivate cooperation amongst each other. Private information should also continue to be considered when examining the dynamics of these networks as it appears to be a primary driver of encouraging individual agency. [ABSTRACT FROM AUTHOR]

The document is an interview with Kali Akuno, co-founder of Cooperation Jackson, discussing cooperatives, the solidarity economy, and social transformation. Akuno emphasizes the importance of working class self-organization and self-management in cooperatives, contrasting it with traditional capitalist ownership structures. He also discusses the historical context of cooperatives and the challenges of transforming the economy to meet community needs. Akuno advocates for a broader understanding of the solidarity economy, focusing on democratic decision-making and collective ownership to address social inequalities and create a more equitable society. [Extracted from the article]

\nPRACTICE IMPLICATIONSResearch on group care has yielded conflicting results, creating a gap in understanding the impact of group care on youth in the child welfare system. One perspective often missing is the perspective of group care workers involved in the daily care of youth in congregate care. This study adds to the limited research in this area by exploring the perspectives of 22 group care workers on how to improve group care for adolescents involved in the US child welfare system. Research questions addressed participants’ views about adolescents’ needs in group care, strategies employed to address these needs, and suggestions for improving group care and the child welfare system more generally. Participants shared that youth in group care have experienced many adverse childhood experiences and rejection, problems that are exacerbated by failed placements. They stressed recognizing youths’ developmental needs and acknowledging their strengths rather than adopting deficit perspectives. Participants also called for increased respect and decision-making authority for group care workers, mainly as manifested through enhanced collaboration with other members of youth support teams, specifically case managers. They also advocated for more comprehensive job training, emphasizing trauma-informed care. Lastly, group care workers advocated incorporating adolescents’ voices in decisions about their lives, including placement choices.Group care workers are integral to adolescents’ care teams, and their perspectives should be valued and respected.Establishing structure and consistency through clear rules and expectations is crucial for youth in residential group care, as it promotes a sense of normalcy and stability.Providing training in evidence-based practices (i.e., trauma-informed care) to group care workers is essential for improving youth outcomes and reducing harmful, punitive approaches.Strengthening relationships between group care workers and case managers can potentially lead to lower staff turnover and a more supportive adolescent environment.Effective group care should respect adolescents’ developmental stage and agency by actively involving them in decisions affecting their futures and well-being, including integrating their voices in placement and permanency decisionsGroup care workers are integral to adolescents’ care teams, and their perspectives should be valued and respected.Establishing structure and consistency through clear rules and expectations is crucial for youth in residential group care, as it promotes a sense of normalcy and stability.Providing training in evidence-based practices (i.e., trauma-informed care) to group care workers is essential for improving youth outcomes and reducing harmful, punitive approaches.Strengthening relationships between group care workers and case managers can potentially lead to lower staff turnover and a more supportive adolescent environment.Effective group care should respect adolescents’ developmental stage and agency by actively involving them in decisions affecting their futures and well-being, including integrating their voices in placement and permanency decisions [ABSTRACT FROM AUTHOR]

The overlap function, a continuous aggregation function, is widely used in classification, decision-making, image processing, etc. Compared to applications, overlap functions have also achieved fruitful results in theory, such as studies on the fundamental properties of overlap functions, various generalizations of the concept of overlap functions, and the construction of additive and multiplicative generators based on overlap functions. However, most of the research studies on the overlap functions mentioned above contain commutativity and continuity, which can limit their practical applications. In this paper, we remove the symmetry and continuity from overlap functions and define discrete pseudo-quasi overlap functions on finite chains. Meanwhile, we also discuss their related properties. Then, we introduce pseudo-quasi overlap functions on sub-chains and construct discrete pseudo-quasi overlap functions on finite chains using pseudo-quasi overlap functions on these sub-chain functions. Unlike quasi-overlap functions on finite chains generated by the ordinal sum, discrete pseudo-quasi overlap functions on finite chains constructed through pseudo-quasi overlap functions on different sub-chains are dissimilar. Eventually, we remove the continuity from pseudo-automorphisms and propose the concept of pseudo-quasi-automorphisms. Based on this, we utilize pseudo-overlap functions, pseudo-quasi-automorphisms, and integral functions to obtain discrete pseudo-quasi overlap functions on finite chains, moreover, we apply them to fuzzy multi-attribute group decision-making. The results indicate that compared to overlap functions and pseudo-overlap functions, discrete pseudo-quasi overlap functions on finite chains have stronger flexibility and a wider range of practical applications. [ABSTRACT FROM AUTHOR]

The household waste (HW) disposal and recycling have become a significant challenge due to increasing quantities of generated household wastes and increased levels of urbanization. Selecting locations/sites for building new HW recycling plant comprises numerous sustainability dimensions, thus, this work aims to develop new decision-making model for evaluating and prioritizing the HW recycling plant locations. This paper is categorized into three phases. First, we propose new improved score function to compare the Fermatean fuzzy numbers. Moreover, an example is presented to validate the effectiveness of proposed score function over the extant ones. Second, we introduce new distance measure to estimate the discrimination degree between Fermatean fuzzy sets (FFSs) and further discuss its advantages over the prior developed Fermatean fuzzy distance measures. Third, we introduce an integrated methodology by combining the method with the removal effects of criteria (MEREC), the stepwise weight assessment ratio analysis (SWARA) and the measurement alternatives and the ranking according to compromise solution (MARCOS) approaches with Fermatean fuzzy (FF) information, and named as the "FF-MEREC-SWARA-MARCOS" framework. In this method, the FF-distance measure is used to find the weights of involved decision-making experts. Moreover, an integrated criteria weighting method is presented with the combination of MEREC and SWARA models under the context of FFSs, while the combined FF-MEREC-SWARA-MARCOS model is applied to evaluate and prioritize the locations for HW recycling plant development, which illustrates its feasibility of the developed framework. Comparative study and sensitivity assessment are conducted to validate the obtained outcomes. This work provides a hybrid decision analysis approach, which marks a significant impact to the HW recycling plant location selection process with uncertain information. [ABSTRACT FROM AUTHOR]

ObjectivesMethodsResultsConclusionMedical complications occur frequently in MCS and influence advance medical decision-making. This study aimed to report on medical complications and advance medical decision-making in a nationwide group of MCS patients.In this descriptive cross-sectional study, clinical and advance medical decision-making characteristics were collected in a survey, completed by the treating physician.The MCS population consisted of 32 patients: 65.6% traumatic etiology, 68.8% male. Patients had a median of five complications: hypertonia/spasticity (81.3%) and pneumonia (50.0%) occurred most frequently. Most patients had curative goals: three patients had a fully curative treatment scenarios, 29 a curative scenario with ≥ 1 treatment restrictions, two a palliative and two a symptomatic scenario. Conversations about advance medical decision-making were complicated by disputes with next of kin, inability to evaluate medical treatment because of medical instability, next of kin not being ready to discuss medical treatment, or a treatment scenario explicitly based on requests of next of kin.Medical complications are common in MCS patients and advance medical decision making was complicated. This legitimates realization of specialized care across acute, post-acute and long-term care. Further longitudinal research into advance medical decision-making is recommended. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated approach to group decision-making (GDM) by using stochastic preference analysis (SPA) and best-worst method (BWM). BWM preparation algorithm is proposed to obtain the best and worst of experts and the relative importance degree. Meanwhile, expert weights model and expert's priority vector model are proposed. Furthermore, the stochastic composite rank acceptability index, stochastic composite expected priority vector, stochastic composite expected rank and stochastic composite confidence factor are developed based on SPA to describe the ranks of alternatives based on SPA. Finally, a group stochastic preference analysis-best worst method (GSPA-BWM) algorithm is developed by analyzing the judgments space through Monte Carlo simulation. The experts can use this method to choose some of the outcomes which they find most useful to make reliable decisions. Examples and comparison analyses show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

The present paper contributes to the literature in two ways. First, it develops a novel p, q Quasirung Orthopair Fuzzy (p, q QOF) based group decision making framework to modify a recently developed multi-criteria decision making (MCDM) model such as Comparisons between Ranked Criteria (COBRAC). Second, the paper ruminates on the Strength-Weakness-Opportunity-Threat (SWOT) of the sugarcane supply chain (SSC) in India vis-à-vis adaptation of the advanced technologies featuring Industry 4.0. To set the sub-factors of various dimensions of SWOT, the theoretical ground of Technology-Organization-Environment (TOE) framework has been used. The sub-factors of SWOT have been derived through an informal in-depth discussion with the experts of the sugar industry. Then using a Likert five-point linguistic scale the experts rated the sub-factors based on their relative importance. To determine the weights the modified COBRAC method has been applied. In subsequent stages the reliability of the model has been tested and sensitivity analysis has been carried out to check the stability of the result. The analysis reveals that while experience, by-product utilization and high demand provides strength and create opportunities for SSC, the areas of concern are lack of variety, fragmented nature of supply chains, shortage of next-gen talent and inadequate infrastructure. However, there are enough promises for SSC. The paper shall provide impetus to strategic decision makers for the sugar industry and puts forth a new decision-making framework for the analysts. [ABSTRACT FROM AUTHOR]

Long‐distance migrations are a striking, and strikingly successful, adaptation for highly mobile terrestrial animals in seasonal environments. However, it remains an open question whether migratory animals are more resilient or less resilient to rapidly changing environments. Furthermore, the mechanisms by which animals adapt or modify their migrations are poorly understood. We describe a dramatic shift of over 500 km in the wintering range of the Western Arctic Herd, a large caribou (Rangifer tarandus) herd in northwestern Alaska, an area that is undergoing some of the most rapid warming on Earth. Between 2012 and 2020, caribou switched from reliably wintering in maritime tundra in the southwesternmost portion of their range to more frequently wintering in mountainous areas to the east. Analysis of this range shift, in conjunction with nearly 200 documented mortality events, revealed that it was both broadly adaptive and likely driven by collective memory of poor winter conditions. Before the range shift, overwinter survival in the maritime tundra was high, routinely surpassing 95%, but falling to around 80% even as fewer animals wintered there. Meanwhile, in the increasingly used mountainous portion of the range, survival was intermediate and less variable across years compared to the extremes in the southern winter ranges. Thus, the shift only imperfectly mitigated overall increased mortality rates. The range shift has also been accompanied by changes in seasonal patterns of survival that are consistent with poorer nutritional intake in winter. Unexpectedly, the strongest single predictor of an individual's probability of migrating south was the overall survival of animals in the south in the preceding winter, suggesting that the range shift is in part driven by collective memory. Our results demonstrate the importance and use of collective decision making and memory for a highly mobile species for improving fitness outcomes in a dynamic, changing environment. [ABSTRACT FROM AUTHOR]

Some new techniques of aggregating hesitant fuzzy numbers (HFNs) by using monotonic dependent OWA (MDOWA) operators are investigated. By utilizing the score value of HFN, the concepts of hesitant fuzzy configuration vector and hesitant fuzzy hybrid configuration vector are proposed. Then, some methods of calculating variable weights related to the MDOWA operators under hesitant fuzzy environments are presented. Further, some operators, including hesitant fuzzy monotonic dependent OWA (HFMDOWA) operators and hesitant fuzzy hybrid monotonic dependent OWA (HFHMDOWA) operators, are developed, such as balanced HFMDOWA operators, rewarded HFMDOWA operators, balanced HFHMDOWA operators, rewarded HFHMDOWA operators, and so on. These developed operators are applied to multiple criteria group decision making (MCGDM), and a novel MCGDM algorithm is presented. By using the presented operators and algorithm, we can obtain symmetric decision-making results. Finally, an application example is provided to demonstrate the effectiveness of the developed MCGDM techniques. [ABSTRACT FROM AUTHOR]

Interval-valued linguistic neutrosophic sets (IVLNSs), Z-numbers, and the trapezium cloud model are powerful tools for expressing uncertainty and randomness. This paper aims to combine these methodologies. First, we review relevant concepts and operators, introducing a novel combination of IVLNSs and Z-numbers, which establishes a new form of expression. Subsequently, we propose the Z-interval-valued linguistic neutrosophic set-trapezium–trapezium cloud (Z-IVLNS-TTC) model, designed to minimize information loss and distortion during quantification. A novel method for calculating the objective weight vector is then developed using multi-objective programming (MOP). Drawing inspiration from the TOPSIS method, we propose a new approach for calculating the distance between Z-IVLNS-TTCs based on the p-norm. Finally, a group decision-making problem is presented to demonstrate the practical application of the proposed method. To validate the effectiveness and feasibility of the method, sensitivity analysis and comparisons with existing approaches are conducted. [ABSTRACT FROM AUTHOR]

Two-phase testing assesses individual performance (phase 1) and then allows collaborative learning within small groups (phase 2). While groups typically outperform individuals, less is known about the social decision schemes that influence member collaboration. In a classroom setting, we compared individual and group performance on a standard test versus a two-phase test (Study 1 n = 180, Study 2 n = 60) to determine the impact of collaborative testing on recalling factual and application based test items. We also assessed various aspects of group dynamics (e.g. liking). Both studies showed several benefits to collaborative testing with few costs: students enjoyed collaborative testing and the performance benefits were stronger for lower scoring students and integrative versus factual test items. [ABSTRACT FROM AUTHOR]

Circular economy and sustainable growth are closely linked as they both aim to reconcile economic development with environmental considerations. The circular economy provides a framework and set of principles for achieving sustainable growth. By adopting circular practices, such as resource efficiency, recycling, and product life extension, economic activities can become more sustainable and contribute to long-term growth. In today's world, consumers have high expectations for companies to be accountable for the environmental and social impact of their products and services. The implementation of Total Quality Management (TQM) in manufacturing can contribute to sustainable growth by enhancing quality, reducing waste, and increasing efficiency. This study proposes a novel methodological framework to establish a comprehensive association between Juran's ten quality principles using a mixed-method sequential approach with an integrated Machine Learning Group Decision-Making (MLGDM) and Interpretive Structural Modeling (ISM)-DEMATEL approach. The framework involves using the MLGDM approach to select the optimal number of experts to develop contextual relationships among the principles. This framework is designed to address the challenge of determining the appropriate number of experts to involve in the decision-making process. Involving too few experts can limit the generalizability of the results, while having too many experts can lead to a high degree of inconsistency and make it challenging to reach a consensus. The MLGDM portion of our framework provides a systematic approach to overcome this challenge and helps supply chain managers and academicians implement quality practices in their organizations. Moreover, although several studies have explored the implementation of TQM practices, there is still a lack of a systematic framework that can fully incorporate Juran's quality principles. To fill this gap, the ISM-DEMATEL approach was then used to explore the causal relationships between these principles. Practitioners from the industry were asked to identify contextual associations among variables, which facilitated a better understanding of these principles. Our results suggest that "Build awareness," "Set goals for improvement," and "Provide training" are strategic requirements for successful TQM implementation, while "Carry out projects to solve problems," "Organize to reach the goals," and "Keep score" are tactical requirements. Furthermore, "Communicate results," "Report progress," "Give recognition," and "Maintain momentum" are operational necessities for TQM implementation. The present study represents a significant step forward in giving a new direction to Juran's ten quality principles and providing a holistic picture to decision-makers. [ABSTRACT FROM AUTHOR]

In light of the burgeoning electric vehicle market, the demand for lithium-ion batteries (LiBs) is on the rise. However, the supply of materials essential for LiBs is struggling to keep pace, posing a significant challenge in meeting the surging market demand. This study offers a viable solution to bolster the dependability of the material supply chain by prioritizing material suppliers who are deeply committed to sustainable practices and performance. We have developed a comprehensive system for evaluating sustainable performance, encompassing three vital dimensions: economic, social and environmental contexts. Then, we introduced a pioneering approach known as the multi-criteria material supplier selection (MCMSS) methodology which amalgamates multi-criteria decision-making techniques with artificial intelligence to effectively generate sustainability performance of suppliers and identify the most suitable supplier, out of all alternatives. Eventually, the supply of four key materials of LiBs is used as illustrative examples to verify the feasibility and rationality of the proposed MCMSS. This work carries significant implications for overseeing the LiB material industry. The MCMSS model offers a solution for the government to establish a comprehensive material supplier database to intelligently supervise the activities of material suppliers and foster collaboration between upstream and downstream enterprises within the LiB industry. [ABSTRACT FROM AUTHOR]

Organizational ties between political parties and interest groups are common in contemporary democracies, but little is known about the political effects of such ties. This article examines whether the strength of organizational ties between parties and interest groups affects the probability of (1) interest group influence on parties, (2) party influence on interest groups, and (3) mutual party–interest group influence in decision-making. Using novel interest group survey data from six democracies, we are the first to systematically examine the relationship between organizational ties and perceived and attributed influence across multiple policy areas. The findings indicate that one-sided influence is more likely when the actors have stronger ties but that such ties also increase the likelihood of influence going both ways. Close party–interest group relationships hence likely involve give-and-take across policy issues. These findings shed important new light on the role of parties and interest groups as intermediaries in democracies. [ABSTRACT FROM AUTHOR]

In this topical review, we integrate 3 concepts—public health practice, community engagement, and cross-sector governance—to consider the following question: What is the underlying relationship between public health and cross-sector governance according to which the field can understand the role of community engagement in achieving health equity? We begin with an overview of public health practice and the practice of community engagement. Next, we position these practices in the broader turn toward cross-sector governance. The integration of these themes reveals that common tools for community engagement fail to address questions about how services should be funded, how resources should be distributed, and which members of the community have a claim to services. We therefore suggest that distinguishing between community engagement for equity and equitable community engagement is a first step toward deepening dialogue about the role of public health in achieving health equity. [ABSTRACT FROM AUTHOR]

The design of a manufacturing supply network (MSN) requires the consideration of decisions made by different groups at multiple levels and their interactions that include potential conflicts. Decisions are typically made based on information from computational simulations that are abstractions of reality and, therefore, embody uncertainty. This necessitates focusing on design space exploration to identify robust satisficing solution sets that are relatively insensitive to uncertainty. Current frameworks that support robust satisficing design space exploration are limited by their capability to support the efficient exploration of multilevel design spaces simultaneously. In this paper, we present the Framework for Robust Multilevel Co-Design Exploration (FRoMCoDE), a decision support framework that allows designers to (i) model decision problems across multiple levels and their interactions, (ii) consider uncertainties in the decision problems, and (iii) visualize and systematically carry out simultaneous exploration of multilevel design spaces, termed co-design exploration. In FRoMCoDE, we combine the coupled-compromise Decision Support Problem construct, where a combination of the Preemptive and Archimedean formulations is used, with robust design constructs and interpretable-Self-Organizing Maps (iSOM)-based visualization to facilitate robust co-design. We use a steel MSN problem with decisions made at two levels to test the framework. Using the problem, we demonstrate FRoMCoDE's efficacy in supporting designers in (i) modeling multilevel decision problems and their interactions, considering the uncertainties, and (ii) the efficient co-design exploration of multilevel design spaces. FRoMCoDE is generic and supports designers in the robust co-design exploration of multilevel systems. [ABSTRACT FROM AUTHOR]

Why do human beings exhibit enduring personality differences, and to what extent are these differences shaped by biological and cultural evolution? Despite ongoing efforts, a consensus framework remains elusive. This paper introduces the Diversity Advantage Theory, partially shifting the focus from how personality impacts individuals to its influence within groups. Through an evolutionary lens, connections among personality, collective cognition, and group dynamics are explored, revealing that diversity in personality traits would tend to enhance collective intelligence and foster deliberation, mitigating group biases. The framework proposes that biologically evolved psychological mechanisms underlying personality, including individual niche-finding, adapt and conform to culturally evolving heuristic personality traits. This co-evolution stabilized the Big Few personality traits in western, educated, industrialized, rich, and democratic (WEIRD) societies, addressing recurring group challenges related to engagement, closure, task, risk, and change. The Diversity Advantage Theory offers a novel explanatory framework for stable personality traits, aligning with biology and testable in contemporary group contexts. At least three novel perspectives are introduced: (1) personality trait dimensions have emerged, in part, to establish normative behavior for functional group processes; (2) normative diversity along trait dimensions enhances collective intelligence; and (3) such diversity should also promote deliberation, mitigate group biases, and improve group decision-making. [ABSTRACT FROM AUTHOR]

Trade-offs between quality and quantity arise in an abundance of contexts concerning group decision making. With the starting point being that group members provide more accurate evidence when they are involved with fewer tasks, team managers often encounter the following dilemma: Should they assign their group members with many tasks (attempting to gather more evidence with lower quality), or with fewer tasks (aiming at receiving less, but more high-quality evidence)? Secondly, what is the optimal way to aggregate the collected evidence from a group, which may be contrasting and varying in accuracy? Should more weight be given to the more accurate group members, or to the larger number of those who provide the same answer? This topic is already studied within the mathematical framework of Terzopoulou and Endriss (2019). In this paper we complement it experimentally, by investigating to what extent people's decision-making patterns are in accordance with the optimal ones proposed by the normative model. Our findings suggest that people understand the task at hand and generally opt for optimal choices, especially in conflict-free cases. Still, a tendency towards overvaluing the importance of additional evidence, despite their accuracy, is observed; this translates into choosing options that align with the majority rule in aggregation problems. [ABSTRACT FROM AUTHOR]

The process of selecting contractors for megaprojects involves inherent uncertainty in judgment and preferences, especially under multiple criteria group decision-making (MCGDM) scenarios. This uncertainty often stems from the ambiguity and hesitation when decision makers (DM) evaluate and prioritize different criteria, causing challenges for the evaluation of contractors' technical capabilities. This study aims to enhance a generalized comparison table (GCT) by applying hesitant fuzzy soft sets to capture this uncertainty. Through the operation of a criteria adjustment algorithm for group consensus, experts-trust network, and the rule of maximum deviation, the GCT is improved by the ability to incorporate objective weights of subjective criteria. The improved GCT provides a transparent evaluation tool and has been tested on contractors' assessments for a mega tunneling project in China. Its practicality is demonstrated by matching the method-assisted decision with the final choice of the project manager. The key research findings highlight the importance of incorporating project-specific criteria, the criteria adjustment algorithm, and the objective weights of criteria to refine MCGDM. The main contributions of this study include (1) a novel framework of the improved GCT for MCGDM, (2) a new criteria system that includes common categories and a project-specific criteria category, (3) a criteria adjustment algorithm to attain group consensus decisions, and (4) objective weights of criteria and DM based on the experts-trust network and maximum deviation model. Practical Applications: Selecting the right contractor for megaprojects can be a daunting task filled with uncertainty. This uncertainty primarily arises from the ambiguity and hesitation when decision-makers evaluate various technical capabilities of different contractors. This study, therefore, aims to improve a method to make the decision-making process more efficient and dependable, particularly in scenarios involving multiple decision-makers and criteria. The improved method incorporates various factors, from technical ability to project-specific criteria, ensuring a comprehensive evaluation of potential contractors. For practicality, it utilizes a fuzzy evaluation language to capture the decision-makers' uncertainty during the evaluation process. Additionally, the study uses a specific algorithm method to generate a group consensus result. The improved method also applies objective weights of both criteria and decision-makers to help reduce subjective evaluation. Through a case study, it is further found that the improved method is beneficial for inviting disciplinary decision-makers to join the megaproject evaluation process. The improved method is recommended for evaluating tenderer megaprojects, especially when decision-makers' backgrounds are diverse and the accurate assessment of technical capabilities is critical. For practitioners interested in applying this method to their own projects, the programming code of this study is available for reference and use. [ABSTRACT FROM AUTHOR]

As an effective tool to show the fuzziness of qualitative information, the probabilistic hesitant fuzzy set (PHFS) can utilize a group of membership degrees with a clear probability distribution to show the opinions of decision-maker (DM). Given this merit, many probabilistic hesitant fuzzy multi-criteria group decision-making (PHF-MCGDM) methods have been designed. However, most of the existing PHF-MCGDM methods have some limitations, including the difficulty of reflecting DMs' ambiguous and hesitant preferences for criteria weights and the inability to comprehensively show the impacts of DMs' irrational behaviors. To address these limitations, this paper develops a novel PHF-MCGDM method that integrates the defining interrelationships between ranked criteria (DIBR) approach and tri-reference point (TRP) theory. First, the PHF-DIBR approach is constructed to determine criteria weights by fully expressing DMs' ambiguous and hesitant preferences for the importance of criteria. Second, the novel probabilistic hesitant fuzzy correlation coefficient (NPHFCC) is developed for deriving the weights of DMs, which remedies the flaws of the existing correlation coefficients (CC). Moreover, TRP theory is used to describe the psychological behavior effects of DMs and derive the order of alternatives. Finally, the applicability of the proposed method is validated by the case about office flooring material selection, while the sensitivity and comparison analyses are also conducted to further prove its advantages and effectiveness. [ABSTRACT FROM AUTHOR]

In China, communities function as grassroots self-governing bodies, and the enhancement of public participation in community governance has remained a central focus of study. This paper applies the Large-Scale Group Decision-Making (LSGDM) method to the process of community self-governance and proposes a dual feedback group consensus decision-making model that takes into account the unique social relations among residents. Firstly, the concept of the Organizational Invisible Field—formed in communities by intangible social capital such as positional power and interpersonal relationships within the organization—is introduced. The definition of Invisible Field Force is utilized to measure the influence of these forms of capital on social relationships. Subsequently, drawing on field dynamic theory, the process by which residents' preferences within the organization are shaped by the action of Invisible Field Force is explored. Secondly, acknowledging that invisible relationships can be affected by dynamic interactions during the decision-making process, the Invisible Field Force change model is constructed. Building on this, a dual feedback consensus coordination mechanism—encompassing both in-organization members and all residents—is designed. Finally, the validity and utility of the model are verified through case studies and sensitivity analyses. [ABSTRACT FROM AUTHOR]

Evaluation of the sustainability of supply chains is a complex decision-making problem. One of the techniques, which are used for assessing the sustainability of supply chains is data envelopment analysis (DEA). Conventional DEA models consider decision making units (DMUs) as black boxes that consume a set of inputs to produce a set of outputs and do not take into consideration the internal interactions of DMUs. Also, they assume inputs and outputs are crisp. In this research, the network DEA (NDEA) model for assessing the sustainability of supply chains in the presence of fuzzy rough data is developed. The main contribution of this paper is to develop a novel NDEA model in the existence of internal and external uncertainties. To validate the proposed model, a case study for evaluating the sustainability of the supply chain in the pasta industry is presented. [ABSTRACT FROM AUTHOR]

As more family physicians choose employment over private practice, more are also wrestling with the question of whether to join a union. Here's what to consider. As more family physicians choose employment over private practice, more are also wrestling with the question of whether to join a union. Here's what to consider. [ABSTRACT FROM AUTHOR]

The article presents a decision‐making model that can be used with blockchain technology. Blockchain is used as an alternative transaction mechanism to authority and the market, where the decision is decentralised within the organisation. Thus, the process is parameterised around the acceptance or not of a project, depending on individual levels of expertise, consensus level and including deliberation time. In addition, commission and omission errors are also evaluated. We show that this technology should be imposed naturally because, at the same level of information, it can obtain better results than any other decision mechanism in a systematic way. In addition, the blockchain ensures that both commission errors and omission errors are reduced with decision times that do not increase the expected opportunity loss on omission errors. [ABSTRACT FROM AUTHOR]

Real-world information is often characterized by uncertainty and partial reliability, which led Zadeh to introduce the concept of Z-numbers as a more appropriate formal structure for describing such information. However, the computation of Z-numbers requires solving highly complex optimization problems, limiting their practical application. Although linguistic Z-numbers have been explored for their computational straightforwardness, they lack theoretical support from Z-number theory and exhibit certain limitations. To address these issues and provide theoretical support from Z-numbers, we propose a Z-number linguistic term set to facilitate more efficient processing of Z-number-based information. Specifically, we redefine linguistic Z-numbers as Z-number linguistic terms. By analyzing the hidden probability density functions of these terms, we identify patterns for ranking them. These patterns are used to define the Z-number linguistic term set, which includes all Z-number linguistic terms sorted in order. We also discuss the basic operators between these terms. Furthermore, we develop a multi-criteria group decision-making (MCGDM) model based on the Z-number linguistic term set. Applying our method to predict the acceptance of academic papers, we demonstrate its effectiveness and superiority. We compare the performance of our MCGDM method with five existing Z-number-based MCGDM methods and eight traditional machine learning clustering algorithms. Our results show that the proposed method outperforms others in terms of accuracy and time consumption, highlighting the potential of Z-number linguistic terms for enhancing Z-number computation and extending the application of Z-number-based information to real-world problems. [ABSTRACT FROM AUTHOR]

Individuals' emotions, such as hesitation and unwavering confidence, can influence the ability of decision-makers (DMs) to make rational judgments. The emotion is always hidden in individual preference series, which is referred to as emotion soft factors, It is a prerequisite for avoiding unfavorable impacts on consensus reaching process. This study focuses on structuring a consensus model with emotion soft factors in linguistic preference time sequence. Specifically, a personalized individual semantics (PIS) learning process is implemented to obtain the personalized numerical scales of DMs' linguistic terms. Subsequently, we propose a consensus model incorporating the consensus measurement and feedback modification phase. In the process, a grey clustering scheme is devised to mine emotion soft factors from DMs' preference sequences and manage individuals in different grey classes. Finally, numerical examples, simulation analysis, and comparison study are presented to illustrate the influence of different parameters and justify the validity of the proposed model. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to establish a synthetic group decision framework based on the Pythagorean fuzzy (PF) set to select the optimal medicine cold chain logistics provider (MCCLP). Fierce market competition makes enterprises must constantly improve every link in the process of enterprise sustainable development. The evaluation of MCCLP in pharmaceutical enterprises is an important link to enhance the comprehensive competitiveness. Because of the fuzziness of expert cognition and the complexity of the decision procedure, PF set can effectively handle the uncertainty and ambiguity in the process of multi-criteria group decision decision-making (MCGDM). Design/methodology/approach: This paper develops an integrated group decision framework through combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique and combined compromise solution (CoCoSo) approach to select a satisfactory MCCLP within PF circumstances. First, the PF set is used to process the ambiguity and uncertainty of the cognition ability of experts. Second, a novel PF knowledge measure is propounded to measure the vagueness of the PF set. Third, a comprehensive criterion weight determination technique is developed through aggregating subjective weights attained utilizing the PF DEMATEL approach and objective weight deduced by knowledge measure method. Furthermore, an integrated MCGDM approach based on synthetic weight and CoCoSo method is constructed. Findings: The outcomes of sensibility analysis and comparison investigation show that the suggested decision framework can help decision experts to choose a satisfactory MCCLP scientifically and reasonably. Accordingly, the propounded comprehensive decision framework can be recommended to enterprises and organizations to assess the MCCLP for their improvement of core competitiveness. Originality/value: MCCLP selection is not only momentous for pharmaceutical enterprises to improve transportation quality and ensure medicine safety but also provides a strong guarantee for enterprises to improve their core competitiveness. Nevertheless, enterprises face certain challenges due to the uncertainty of the assessment environment as well as human cognition in the process of choosing a satisfactory MCCLP. PF set possesses a formidable capability to address the uncertainty and imprecision information in the process of MCGDM. Therefore, pharmaceutical enterprises can implement the proposed method to evaluate the suppliers to further improve the comprehensive profit of enterprises. [ABSTRACT FROM AUTHOR]

In group decision making, the decision participants may exhibit various interaction behaviors, some of them are efficient at facilitating the efficiency of consensus reaching process. Motivated by the view of this, a bi-level consensus model with maximum return and minimum cost considering interaction and transaction behaviors (MRMC-IT) is proposed in the way of adopting the Choquet integral. The bi-level feedback mechanism based on the adjustment of opinions guarantees the interaction between the moderator in the upper level and the decision makers in the lower level. Meanwhile, a transaction mechanism of discourse weights is elicited to present the trading behaviors between the pairwise decision makers. Additionally, in order to solve the complex proposal, a bi-level interactive approach based on satisfactory degree is applied to obtain a satisfactory solution for all decision participants. Finally, an application of sewage discharge rights trading is conducted to demonstrate the validity and the rationality of the proposal. [ABSTRACT FROM AUTHOR]