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Emergencies are typically characterized by abruptness, time urgency, and complexity, which give rise to challenges such as incomplete information, compromised information effectiveness, and reduced efficiency. To address these issues, this study proposes a novel multi-criteria group emergency decision-making (MCGEDM) method considering knowledge granularity. Within the framework of a hierarchical criterion system (HCS), decision makers' (DMs') judgment information is extracted using belief distributions (BDs) on knowledge chunks, based on the conceptualization of knowledge granularity. This enables DMs to make judgments as effectively as possible, thereby improving efficiency in terms of time and enhancing the effectiveness of information. The generalized combination (GC) rule is applied for individual information fusion within basic nests, demonstrating internal revision and complementation of information. Automatic parameter determination methods are proposed to enhance the effectiveness of information and the efficiency of MCGEDM. Finally, the proposed method is demonstrated through a simulative case of an oil spill emergency, and the subsequent sensitivity analysis and comparisons verify its feasibility and effectiveness. • An emergency decision-making method is proposed considering knowledge granularity. • The generalized combination rule for evidence is combined with knowledge granularity. • Decision makers are allowed to only make judgment within their capacity. • The presented information fusion promotes internal revision and complementation. • The offered automatic determination of parameters benefits efficient decision-making. [ABSTRACT FROM AUTHOR]

In large-scale group decision-making, participants with large differences in knowledge structures and educational backgrounds are unlikely to give an accurate evaluation of each criterion of product ideas. To solve this problem and to effectively extract and combine uncertainty in the evaluation information to ultimately obtain a ranking of product ideas, we propose a dynamic intelligent integration recommendation method for product ideas. First, we construct a new evaluation criteria system for product ideas that includes input criteria and output criteria. Second, we describe steps for static information extraction and information combination. We use the basic probability assignment function as an information extraction method to effectively capture and accurately reflect the authenticity of experts' evaluation. For information combination, we employ the analytical evidence reasoning rule for both individual and group combination of evaluation information. On this basis, we can achieve real-time updating of ideas, the screening of effective ideas, and a dynamic intelligence recommendation method. We apply our method to an illustrative example to demonstrate our method's practical use. [ABSTRACT FROM AUTHOR]

• BPA function is used to extract experts' opinions at different levels of subsystems. • Experts are divided into clusters to coordinate different expert opinions. • Intrasubgroup and intersubgroup consensus are designed in hierarchical DEMATEL. • Automatic correction mechanism is used to enhance the efficiency of consensus-reaching. • Large-scale group hierarchical DEMATEL method with consensus reaching are proposed. Decision-making trial and evaluation laboratory (DEMATEL) is widely used because of its ability to effectively analyze nonlinear relationships between factors in complex systems. With the increasing complexity of decision-making problems, large-scale group decision-making (LSGDM) has become the norm. Most existing DEMATEL methods are only suitable for small-scale groups and simple systems. This study, therefore, proposes a large-scale group hierarchical DEMATEL method that considers consensus reaching. The DEMATEL method for LSGDM faces three challenges: large differences in knowledge structures, difficulty coordinating expert opinions, and slow group-consensus convergence. To address these challenges, first, we use hierarchical decomposition to decompose the complex system into simple systems with different levels to reduce the difficulty of decision-making in complex systems. Second, considering the limitations of expert knowledge and experience, we use the basic probability assignment function to extract the opinions of experts at different levels of subsystems and factors. Third, we divide experts into different clusters using K-means clustering to solve the problem of difficult expert-opinion coordination. Fourth, we design two types of consensuses (intrasubgroup and intersubgroup consensus) and an efficient new type of opinion autocorrection mechanism to solve the problem of the slow convergence of intragroup consensus and improve the efficiency of consensus reaching. Finally, we demonstrate the superiority of the proposed method through data analysis and method comparison. [ABSTRACT FROM AUTHOR]

The decision-making trial and evaluation laboratory (DEMATEL) method is a hot issue in industrial engineering field for it can help determine critical factors in complex system. Although lots of efforts have been spent on improving the DEMATEL, they are just the extensions from the subjective perspective but lack of the objective perspective. This study focuses on providing a new improved DEMATEL method based on both subjective experience and objective data. In order to reasonably determine the initial direct-relation (IDR) matrix, the basic probability assignment (BPA) function is employed to extract expert experience and the Dempster's rule with Shafer's discounting is employed to make combination to derive the subjective IDR matrix. Then the path analysis is suggested to test each possible influence relation included in the subjective IDR matrix, and the objective IDR matrix consisting of path coefficients of any two factors is derived by training the sample data of factors. Following the principle of one-vote negation, the Dempster's rule is once again used to make combination for two kinds of IDR matrices, based on which an algorithm for the new improved DEMATEL is summarized to find the major factors in a complex system. Finally, numerical comparison and discussion are proposed to demonstrate the applicability and superiority of the prosed method. • Subjective initial direct-relation matrix is constructed with expert experience. • Objective initial direct-relation matrix is constructed with objective data. • Two kinds of initial direct-relation matrices are combined by Dempster's rule. • Algorithm for the new improved DEMATEL is constructed to find the major factors. [ABSTRACT FROM AUTHOR]

Highlights • Basic probability assignment function is used to extract expert judgment information. • Two types of super fuzzy relationship matrices on grade's power set are constructed. • Multi-objective programming is established to derive belief distribution on grades. • Algorithm is proposed to solve group fuzzy comprehensive evaluation under ignorance. • The proposed method is compared with three kinds of relevant methods. Abstract This paper aims at solving such a group fuzzy comprehensive evaluation (FCE) problem that the global or local ignorance may exist in judgments made by experts and the importance degrees of experts are different. The basic probability assignment (BPA) function is used to extract the expert's judgment information and the super fuzzy relationship matrices consisting of the individual type and the general type are constructed by Shafer's discounting and Dempster's rule. Then each type of super fuzzy relationship matrix is combined with factor weight set via a specified fuzzy operator and the comprehensive evaluation result that is a belief distribution on the power set of grade levels is obtained. A multi-objective programming model is established to compute the optimal belief distribution on each grade level and an algorithm is summarized to derive the final grade level that the evaluated alternative belongs to. Moreover, the numerical comparisons between the proposed method and relevant existing methods are given to clarify the advantages of the proposed method. Finally, an illustrative example is provided to demonstrate the applicability of the proposed method and algorithm. It is worth noting that the proposed method can be easily converted into a core algorithm, which is benefit for developing fuzzy expert system from the perspective of ignorance, and thus it has an important impact and significance on expert and intelligent systems. [ABSTRACT FROM AUTHOR]

Highlights • Information extraction mechanism is introduced to match expert knowledge structure. • Expert reliability and attribute weight are both used to discount decision information. • The analytical ER rule-based models are established to make information combination. • Programming model satisfied experts is used to derive the optimal evaluation results. • Algorithm is proposed to solve the LMGDM problems. Abstract The large-scale multiattribute group decision-making (LMGDM) consisting of at least 20 experts has been widely popular in recent years. Although lots of efforts have been spent on improving the LMGDM, the subjective factors such as experts' domain knowledges and bounded rationalities are still not well considered. This study focuses on providing a new LMGDM method by considering expert knowledge structure. An information extraction mechanism providing three kinds of inference ways including singleton attribute inference, local integral inference and global integral inference is introduced to ensure the assessments made by each expert with interval-valued intuitionistic fuzzy values (IVIFV) to be valid. Then a transformation is introduced to derive interval-valued basic probability assignment (BPA) function from the IVIFV, based on which expert reliability and attribute weight can be both reflected by evidential reasoning (ER) discounting. A pair of nonlinear optimization models that are extended by the analytical ER rule are established to make attribute fusion for expert and group fusion for alternative. An algorithm is summarized to solve the LMGDM problems by considering expert knowledge structure. Finally, an illustrative example as well as discussions is provided to demonstrate the applicability of the proposed method and algorithm. [ABSTRACT FROM AUTHOR]

Large-scale multiattribute group decision-making (LMGDM) requires a large number of participants with different knowledge structures. This study proposed an LMGDM consensus-reaching method in which the experts' knowledge structures are fully considered. An information extraction mechanism is constructed to extract incomplete inference information with the form of belief distribution (BD), and the Dempster–Shafer theory of evidence is adopted to make discounting and combinations for the BDs. To reduce their number, the experts are classified into different clusters by using the extended K-means approach, and two levels of consensus measures are both calculated to determine whether the experts involved in each cluster have reached a satisfactory level of consensus. If that consensus level is not reached, a feedback mechanism is activated to advise the identified experts to adjust their assessments, which allows them to change clusters during the consensus-reaching process. Through repeating the feedback mechanism, the assessments are improved until the satisfactory consensus levels are reached. A multi-objective linear programming method is established to obtain the optimal solution that satisfies all clusters as much as possible. Finally, a numerical comparison and discussion are undertaken to demonstrate the superiority of the proposed method. • Knowledge structures of experts are considered in dealing with the LMGDM problem. • Dempster–Shafer theory is employed to express and fuse information in the LMGDM. • Several clusters are allowed to help reach consensus and experts can change their clusters. • Multi-objective programming is established to make a coordinated decision for all clusters. [ABSTRACT FROM AUTHOR]

Kelley's attribution theory has been widely popular in recent years. Lots of efforts have been spent on improving it with the assumption that there is only one expert to make attributions and the expert is assumed to be omniscient and omnipotent. However, such an assumption hardly exists in reality for the reason that the knowledge of each expert to make judgments is always limited. In order to solve this problem, this paper proposes a group inference method under the framework of Kelley's attribution theory based on Dempster–Shafer theory of evidence. An information extraction mechanism is introduced to ensure that the real judgments of each expert can be well described. Then Shafer's discounting is used to generate the basic probability assignment (BPA) functions by integrating the weights of experts on different criteria into the judgments of experts. The Dempster's rule is employed to make fusion for the BPA functions, and a consensus reaching model which can increase the satisfaction degrees of group decision as much as possible is established to determine the probabilities of external and internal causes for the evaluated behavior. Finally, an algorithm is summarized, and illustrative example and discussion are provided to demonstrate its applicability. • Information extraction mechanism is introduced to ensure judgments to be valid. • Dempster's rule-based method is proposed to make fusion for attribution theory. • Consensus reaching is introduced to increase satisfaction degree of group decision. • Group inference algorithm of attribution theory is presented with the DST. [ABSTRACT FROM AUTHOR]