Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns,leading to non-cooperative behaviors during the consensus-reaching process.Many studies on noncooper...Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns,leading to non-cooperative behaviors during the consensus-reaching process.Many studies on noncooperative behavior management assumed that the maximumdegree of cooperation of experts is to totally accept the revisions suggested by the moderator,which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process.In addition,when grouping a large group into subgroups by clustering methods,existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously.In this study,we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensusmodel taking into account the altruistic behaviors of experts.First,we cluster experts into subgroups by a constrained Kmeans clustering algorithm according to the opinion similarity and trust relationship of experts.Then,we calculate the weights of experts and clusters based on the centrality degrees of experts.Next,to enhance the quality of consensus reaching,we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts.A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making.The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.展开更多
Information is a key factor in emergency management, which helps decision makers to make effective decisions. In this paper, aiming at clarifying the information aggregation laws, and according to the characteristic o...Information is a key factor in emergency management, which helps decision makers to make effective decisions. In this paper, aiming at clarifying the information aggregation laws, and according to the characteristic of emergency information, information relative entropy is applied in the information aggregation to establish the information aggregation model of emergency group decision-making. The analysis shows that support and credibility of decision rule are the two factors in information aggregation. The results of four emergency decision-making groups in case study support the analysis in the paper.展开更多
In this paper, a new decision making approach is proposed for the multi-attribute large group emergency decision-making problem that attribute weights are unknown and expert preference information is expressed by gene...In this paper, a new decision making approach is proposed for the multi-attribute large group emergency decision-making problem that attribute weights are unknown and expert preference information is expressed by generalized interval-valued trapezoidal fuzzy numbers （GITFNs）. Firstly, a degree of similarity formula between GITFNs is presented. Secondly, expert preference information on different alternatives is clustered into several aggregations via the fuzzy clustering method. As the clustering proceeds, an index of group preference consistency is introduced to ensure the clustering effect, and then the group preference information on different alternatives is obtained. Thirdly, the TOPSIS method is used to rank the alternatives. Finally, an example is taken to show the feasibility and effectiveness of this approach. These method can ensure the consistency degree of group preference, thus decision efficiency of emergency response activities can be improved.展开更多
In emergency decision making(EDM),it is necessary to generate an effective alternative quickly.Case-based reasoning(CBR)has been applied to EDM;however,choosing the most suitable case from a set of similar cases after...In emergency decision making(EDM),it is necessary to generate an effective alternative quickly.Case-based reasoning(CBR)has been applied to EDM;however,choosing the most suitable case from a set of similar cases after case retrieval remains challenging.This study proposes a dynamic method based on case retrieval and group decision making(GDM),called dynamic casebased reasoning group decision making(CBRGDM),for emergency alternative generation.In the proposed method,first,similar historical cases are identified through case similarity measurement.Then,evaluation information provided by group decision makers for similar cases is aggregated based on regret theory,and comprehensive perceived utilities for the similar cases are obtained.Finally,the most suitable historical case is obtained from the case similarities and the comprehensive perceived utilities for similar historical cases.The method is then applied to an example of a gas explosion in a coal company in China.The results show that the proposed method is feasible and effective in EDM.The advantages of the proposed method are verified based on comparisons with existing methods.In particular,dynamic CBRGDM can adjust the emergency alternative according to changing emergencies.The results of application of dynamic CBRGDM to a gas explosion and comparison with existing methods verify its feasibility and practicability.展开更多
基金supported by the National Natural Science Foundation of China (Nos.71771156,71971145,72171158).
文摘Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns,leading to non-cooperative behaviors during the consensus-reaching process.Many studies on noncooperative behavior management assumed that the maximumdegree of cooperation of experts is to totally accept the revisions suggested by the moderator,which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process.In addition,when grouping a large group into subgroups by clustering methods,existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously.In this study,we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensusmodel taking into account the altruistic behaviors of experts.First,we cluster experts into subgroups by a constrained Kmeans clustering algorithm according to the opinion similarity and trust relationship of experts.Then,we calculate the weights of experts and clusters based on the centrality degrees of experts.Next,to enhance the quality of consensus reaching,we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts.A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making.The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.
文摘Information is a key factor in emergency management, which helps decision makers to make effective decisions. In this paper, aiming at clarifying the information aggregation laws, and according to the characteristic of emergency information, information relative entropy is applied in the information aggregation to establish the information aggregation model of emergency group decision-making. The analysis shows that support and credibility of decision rule are the two factors in information aggregation. The results of four emergency decision-making groups in case study support the analysis in the paper.
基金supported by a grant from Natural Science Foundation in China(71171202, 71171201,71210003)the Science Foundation for National Innovation Research Group in China(71221061)Key Project for National Natural Science Foundation in China (71431006)
文摘In this paper, a new decision making approach is proposed for the multi-attribute large group emergency decision-making problem that attribute weights are unknown and expert preference information is expressed by generalized interval-valued trapezoidal fuzzy numbers （GITFNs）. Firstly, a degree of similarity formula between GITFNs is presented. Secondly, expert preference information on different alternatives is clustered into several aggregations via the fuzzy clustering method. As the clustering proceeds, an index of group preference consistency is introduced to ensure the clustering effect, and then the group preference information on different alternatives is obtained. Thirdly, the TOPSIS method is used to rank the alternatives. Finally, an example is taken to show the feasibility and effectiveness of this approach. These method can ensure the consistency degree of group preference, thus decision efficiency of emergency response activities can be improved.
基金partly supported by the National Natural Science Foundation of China under the Grant Nos.71371053 and 71902034Humanities and Social Sciences Foundation of Chinese Ministry of Education,No.20YJC630229+1 种基金Humanities and Social Science Foundation of Fujian Province,No.FJ2019B079Science and Technology Development Center of Chinese Ministry of Education.No.2018A0I019.
文摘In emergency decision making(EDM),it is necessary to generate an effective alternative quickly.Case-based reasoning(CBR)has been applied to EDM;however,choosing the most suitable case from a set of similar cases after case retrieval remains challenging.This study proposes a dynamic method based on case retrieval and group decision making(GDM),called dynamic casebased reasoning group decision making(CBRGDM),for emergency alternative generation.In the proposed method,first,similar historical cases are identified through case similarity measurement.Then,evaluation information provided by group decision makers for similar cases is aggregated based on regret theory,and comprehensive perceived utilities for the similar cases are obtained.Finally,the most suitable historical case is obtained from the case similarities and the comprehensive perceived utilities for similar historical cases.The method is then applied to an example of a gas explosion in a coal company in China.The results show that the proposed method is feasible and effective in EDM.The advantages of the proposed method are verified based on comparisons with existing methods.In particular,dynamic CBRGDM can adjust the emergency alternative according to changing emergencies.The results of application of dynamic CBRGDM to a gas explosion and comparison with existing methods verify its feasibility and practicability.
