Extension of TOPSIS method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

2009 ◽  
Vol 25 (3) ◽  
pp. 623-634 ◽  
Author(s):  
Gui-Wu Wei
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Topsis Method ◽  
Multiple Attribute ◽  
Incomplete Weight ◽  
Incomplete Weight Information

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.

Reducing the Risk of Wrong Choice in Group Decision Making by Optimal Weight Allocating to Decision Makers

2018 ◽  
Vol 7 (2) ◽  
pp. 1-23 ◽  
Author(s):  
Mohammad Azadfallah
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Potential Risk ◽  
Group Decision ◽  
Decision Makers ◽  
Topsis Method ◽  
Numerical Examples ◽  
Multiple Attribute ◽  
Key Issues ◽  
The Ideal

How to determine a weight for decision makers (DMs) is one of the key issues in Multiple Attribute Group Decision Making (MAGDM). While, some experts (or DMs) clearly wiser and more powerful in such matters than others, it has often seen that experts play their roles with same weights of importance. Meanwhile, it will lead to the wrong choice (or decision risk) and loss of values. Since, in the absence of any other standards about how to reduce this potential risk for bias, in this article, based on judgment matrices and error analysis, the author presents two new algorithm taken from crisp (the correlation-based approach) and interval (the ideal-based approach) TOPSIS method, respectively. Finally, two numerical examples are given to demonstrate the feasibility of the developed method.

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