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Mathematical Problems in Engineering
Volume 2017, Article ID 1438425, 18 pages
Research Article

A Scientific Decision Framework for Supplier Selection under Interval Valued Intuitionistic Fuzzy Environment

1School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India
2School of Management, SASTRA University, Thanjavur, Tamil Nadu, India

Correspondence should be addressed to R. Krishankumar;

Received 14 May 2017; Revised 29 July 2017; Accepted 7 August 2017; Published 3 October 2017

Academic Editor: Love Ekenberg

Copyright © 2017 R. Krishankumar 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.


This paper proposes a new scientific decision framework (SDF) under interval valued intuitionistic fuzzy (IVIF) environment for supplier selection (SS). The framework consists of two phases, where, in the first phase, criteria weights are estimated in a sensible manner using newly proposed IVIF based statistical variance (SV) method and, in the second phase, the suitable supplier is selected using ELECTRE (ELimination and Choice Expressing REality) ranking method under IVIF environment. This method involves three categories of outranking, namely, strong, moderate, and weak. Previous studies on ELECTRE ranking reveal that scholars have only used two categories of outranking, namely, strong and weak, in the formulation of IVIF based ELECTRE, which eventually aggravates fuzziness and vagueness in decision making process due to the potential loss of information. Motivated by this challenge, third outranking category, called moderate, is proposed, which considerably reduces the loss of information by improving checks to the concordance and discordance matrices. Thus, in this paper, IVIF-ELECTRE (IVIFE) method is presented and popular TOPSIS method is integrated with IVIFE for obtaining a linear ranking. Finally, the practicality of the proposed framework is demonstrated using SS example and the strength of proposed SDF is realized by comparing the framework with other similar methods.