Paper Info
Title
Minimum Weighted Minkowski Distance Power Models For Intuitionistic Fuzzy Madm With Incomplete Weight Information
Abstract
Owing to more vague concepts frequently represented in decision data, intuitionistic fuzzy sets (IFSs) are more fliexibly used to model real-life decision situations. At the same time, with ever increasing complexity in many decision situations in reality, there are often some challenges for a decision maker to provide complete attribute preference information, i.e., the weights may be completely unknown or partially known. The aim of this paper is to develop an effiective method for solving intuitionistic fuzzy multi-attribute decision making (MADM) problems with incomplete weight information. In this method, ratings of alternatives on attributes are expressed with IFSs. The multi-objective programming models are established to calculate unknown weights by using weight information partially known a priori. The derived minimum weighted Minkowski distance power models are used to determine the unknown weights and to generate the ranking order of the alternatives simultaneously. The proposed models are easily extended to intuitionistic fuzzy MADM problems with different weight information structures. An example of the supplier selection problem is examined to demonstrate applicability and flexibility of the proposed models and method.
Year
DOI
Venue
2017
10.1142/S0219622014500321
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Keywords
Field
DocType
Intuitionistic fuzzy set, multi-attribute decision making, uncertainty modeling, multi-objective programming, distance measure
Mathematical optimization,Minkowski distance,Programming paradigm,Ranking,Effective method,Fuzzy logic,A priori and a posteriori,Fuzzy set,Weighted sum model,Artificial intelligence,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
16
5
0219-6220
Citations 
PageRank 
References 
1
0.35
26
Authors
2
Name
Order
Citations
PageRank
Deng-Feng Li196846.12
Shu-Ping Wan246425.91