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Mathematical Problems in Engineering
Volume 2015, Article ID 791204, 13 pages
http://dx.doi.org/10.1155/2015/791204
Research Article

A Selection Method Based on MAGDM with Interval-Valued Intuitionistic Fuzzy Sets

1College of Science, Guilin University of Technology, Guilin 541004, China
2College of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China
3College of Information Science and Engineering, Guilin University of Technology, Guilin 541002, China

Received 3 December 2014; Accepted 11 May 2015

Academic Editor: Julien Bruchon

Copyright © 2015 Gai-Li Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As the cloud computing develops rapidly, more and more cloud services appear. Many enterprises tend to utilize cloud service to achieve better flexibility and react faster to market demands. In the cloud service selection, several experts may be invited and many attributes (indicators or goals) should be considered. Therefore, the cloud service selection can be regarded as a kind of Multiattribute Group Decision Making (MAGDM) problems. This paper develops a new method for solving such MAGDM problems. In this method, the ratings of the alternatives on attributes in individual decision matrices given by each expert are in the form of interval-valued intuitionistic fuzzy sets (IVIFSs) which can flexibly describe the preferences of experts on qualitative attributes. First, the weights of experts on each attribute are determined by extending the classical gray relational analysis (GRA) into IVIF environment. Then, based on the collective decision matrix obtained by aggregating the individual matrices, the score (profit) matrix, accuracy matrix, and uncertainty (risk) matrix are derived. A multiobjective programming model is constructed to determine the attribute weights. Subsequently, the alternatives are ranked by employing the overall scores and uncertainties of alternatives. Finally, a cloud service selection problem is provided to illustrate the feasibility and effectiveness of the proposed methods.