Green Supplier Selection Using Improved TOPSIS and Best-Worst Method Under Intuitionistic Fuzzy Environment

Informatica ◽  
2018 ◽  
Vol 29 (4) ◽  
pp. 773-800 ◽  
Author(s):  
Zhang-Peng Tian ◽  
Hong-Yu Zhang ◽  
Jian-Qiang Wang ◽  
Tie-Li Wang
Keyword(s):  
Supplier Selection ◽  
Green Supplier Selection ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Best Worst Method

Green supplier selection in steel industry with intuitionistic fuzzy Taxonomy method

2020 ◽  
Vol 39 (5) ◽  
pp. 7247-7258
Author(s):  
Lu Xiao ◽  
Siqi Zhang ◽  
Guiwu Wei ◽  
Jiang Wu ◽  
Cun Wei ◽  
...  
Keyword(s):  
Steel Industry ◽  
Supplier Selection ◽  
Correlation Method ◽  
Green Supply Chain ◽  
Weighting Method ◽  
Green Supplier Selection ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Multiple Attribute ◽  
Criteria Importance

Since people around the world have gradually attached importance to resource conservation, various countries are actively taking measures to promote environmental protection and sustainable development. Green supply chain management (GSCM) have emerged in this context. Thus, in this essay, a novel intuitionistic fuzzy multiple attribute group decision making (MAGDM) method is designed to tackle this issue. First of all, CRITIC (Criteria Importance Through Inter-criteria Correlation) method is utilized to determine the weights of criteria. Later, the conventional Taxonomy method is extended to the intuitionistic fuzzy environment to compute the value of development attribute of each supplier. Then, the optimal one can be determined. Eventually, an application about green supplier selection in steel industry is presented, and a comparative analysis is made to demonstrate the superiority of the proposed method. The main features of the proposed algorithm are that they provide a practical solution for selecting GSCM and presents an objective weighting method to enhance the effectiveness of the algorithm.

scholarly journals Green Supplier Selection Based on DEA Model in Interval-Valued Pythagorean Fuzzy Environment

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 108001-108013 ◽  
Author(s):  
Mei-Qin Wu ◽  
Can-Hui Zhang ◽  
Xiao-Na Liu ◽  
Jian-Ping Fan
Keyword(s):  
Supplier Selection ◽  
Dea Model ◽  
Green Supplier Selection ◽  
Fuzzy Environment ◽  
Interval Valued

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

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
R. Krishankumar ◽  
K. S. Ravichandran ◽  
R. Ramprakash
Keyword(s):  
Supplier Selection ◽  
Second Phase ◽  
Decision Framework ◽  
Loss Of Information ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Criteria Weights ◽  
Two Phases ◽  
Scientific Decision ◽  
Interval Valued

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.

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