scholarly journals A Dynamic Emergency Decision-Making Method Based on Group Decision Making with Uncertainty Information

2020 ◽  
Vol 11 (5) ◽  
pp. 667-679
Author(s):  
Jing Zheng ◽  
Yingming Wang ◽  
Kai Zhang ◽  
Juan Liang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Gas Explosion ◽  
Historical Case ◽  
Emergency Decision Making ◽  
Emergency Decision ◽  
Case Based

Abstract 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 case-based 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.

Case-Based Reasoning Model with Genetic Algorithms, Group Decision-Making and Template Reduction

2016 ◽  
Vol 25 (02) ◽  
pp. 1550032 ◽  
Author(s):  
Aijun Yan ◽  
Hairuo Song ◽  
Pu Wang
Keyword(s):  
Decision Making ◽  
Genetic Algorithms ◽  
Group Decision Making ◽  
Nearest Neighbor ◽  
Group Decision ◽  
Small Samples ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Attribute Weights ◽  
Case Based

Case retrieval, case reuse and case retention are critical to the reasoning performance of the traditional case-based reasoning (CBR) model. In this paper, the integrated use of template reduction technology (TR), genetic algorithms (GA), nearest neighbor (NN) rules and group decision-making (GDM) establishes the CBR-GDM model. First, the TR method of the case base is introduced. Then, an attribute weights optimization using GA is discussed in the case retrieval phase. After that, a case reuse method is carried out with NN and GDM. Finally, 10 data sets from UCI are used to carry out a comparison experiment by 5-fold cross-validation. The classification accuracy rate and the classification efficiency are analyzed under the small samples, before and after the data reduction. The results show that, combined with TR, GA and GDM, the pattern classification performance by CBR can be improved.

Heterogeneous multi-attribute case retrieval method based on group decision making using incomplete weight information

2021 ◽  
pp. 1-13
Author(s):  
Kai Zhang ◽  
Jing Zheng ◽  
Ying-Ming Wang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Interval Numbers ◽  
Retrieval Method ◽  
Incomplete Weight ◽  
Incomplete Weight Information ◽  
Evaluation Information

Case-based reasoning (CBR) is one of the most popular methods used in emergency decision making (EDM). Case retrieval plays a key role in EDM processes based on CBR and usually functions by retrieving similar historical cases using similarity measurements. Decision makers (DMs), thus, choose the most appropriate historical cases. Although uncertainty and fuzziness are present in the EDM process, in-depth research on these issues is still lacking. In this study, a heterogeneous multi-attribute case retrieval method based on group decision making (GDM) with incomplete weight information is developed. First, the case similarities between historical and target cases are calculated, and a set of similar historical cases is constructed. Six formats of case attributes are considered, namely crisp numbers, interval numbers, linguistic variables, intuitionistic fuzzy numbers, single-valued neutrosophic numbers (NNs) and interval-valued NNs. Next, the evaluation information from the DMs is expressed using single-valued NNs. Additionally, the evaluation utilities of similar historical cases are obtained by aggregating the evaluation information. The comprehensive utilities of similar historical cases are obtained using case similarities and evaluation utilities. In this process, the weights of incomplete information are determined by constructing optimization models. Furthermore, the most appropriate similar historical case is selected according to the comprehensive utilities. Finally, the proposed method is demonstrated using two examples; its performance is then compared with those of other similar methods to demonstrate its validity and efficacy.

Research on the Auxiliary Emergency Decision-Making System of Urban Mass Transit on the Basis of Case-Based Reasoning

2013 ◽  
Vol 361-363 ◽  
pp. 2127-2133
Author(s):  
Hong Fei Yu ◽  
Yong Qin ◽  
Zi Yang Wang ◽  
Zhong Xin Zhao ◽  
Jun Li
Keyword(s):  
Decision Making ◽  
Expert Knowledge ◽  
Mass Transit ◽  
System Structure ◽  
Case Based Reasoning ◽  
Urban Mass Transit ◽  
Emergency Decision Making ◽  
Function Module ◽  
Emergency Decision ◽  
Case Based

To meet the requirement of networked urban mass transit, the characteristics of urban mass transit emergency decision-making was analyzed, draw lessons from the thought of case-based reasoning technology, the method which combine CBR technology with expert knowledge was proposed. On the basis of which, the emergency decision-making support system was designed, analyzed the system structure and function module, discussed the key technologies involved in the system, focused on the representation of retrieval technology and case contingency plans and case. Provides support for the modernization of urban mass transit emergency platform.

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