Novel q-rung orthopair fuzzy interaction aggregation operators and their application to low-carbon green supply chain management

2021 ◽  
pp. 1-18
Author(s):  
Muhammad Riaz ◽  
Harish Garg ◽  
Hafiz Muhammad Athar Farid ◽  
Muhammad Aslam
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supplier Selection ◽  
Weighted Averaging ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Environmental Concerns ◽  
Low Carbon ◽  
Chain Management ◽  
Carbon Supply

The low-carbon supply chain management is big a challenge for the researchers due to the rapid increase in global warming and environmental concerns. With the advancement of the environmental concerns and social economy, it is an unavoidable choice for a business to achieve sustainable growth for low-carbon supply chain management. Since the root of the chain depends upon the supplier selection and choosing an excellent low-carbon supply. Green supplier selection is one of the most crucial activities in low-carbon supply chain management, it is critical to develop rigorous requirements and a system for selection in low-carbon green supply chain management (LCGSCM). A q-rung orthopair fuzzy number (q-ROFN) is pair of membership degree (MD) and non-membership degrees (NMD) which is reliable to address uncertainties in the various real-life problems. This article sets out a decision analysis approach for interactions between MDs and NMDs with the help of q-ROFNs. For this objective, we develop new aggregation operators (AOs) named as, q-rung orthopair fuzzy interaction weighted averaging (q-ROFIWA) operator, q-rung orthopair fuzzy interaction ordered weighted averaging (q-ROFIOWA) operator, q-rung orthopair fuzzy interaction hybrid averaging (q-ROFIHA) operator, q-rung orthopair fuzzy interaction weighted geometric (q-ROFIWG) operator, q-rung orthopair fuzzy interaction ordered weighted geometric (q-ROFIOWG) operator and q-rung orthopair fuzzy interaction hybrid geometric (q-ROFIHG) operator. These AOs define an advanced approach for information fusion and modeling uncertainties in multi-criteria decision-making (MCDM). At the end, a robust MCDM approach based on newly developed AOs is developed. Some significant properties of these AOS are analyzed and the efficiency of the developed approach is assessed with a practical application towards sustainable low-carbon green supply chain management.

scholarly journals A Fuzzy-Grey Multicriteria Decision Making Approach for Green Supplier Selection in Low-Carbon Supply Chain

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qinghua Pang ◽  
Tiantian Yang ◽  
Mingzhen Li ◽  
Yi Shen
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Supplier Selection ◽  
Multicriteria Decision Making ◽  
Low Carbon ◽  
Chain Management ◽  
Multicriteria Decision ◽  
Green Supplier Selection ◽  
Carbon Supply

Due to the increasing awareness of global warming and environmental protection, many practitioners and researchers have paid much attention to the low-carbon supply chain management in recent years. Green supplier selection is one of the most critical activities in the low-carbon supply chain management, so it is important to establish the comprehensive criteria and develop a method for green supplier selection in low-carbon supply chain. The paper proposes a fuzz-grey multicriteria decision making approach to deal with these problems. First, the paper establishes 4 main criteria and 22 subcriteria for green supplier selection. Then, a method integrating fuzzy set theory and grey relational analysis is proposed. It uses the membership function of normal distribution to compare each supplier and uses grey relation analysis to calculate the weight of each criterion and improves fuzzy comprehensive evaluation. The proposed method can make the localization of individual green supplier more objectively and more accurately in the same trade. Finally, a case study in the steel industry is presented to demonstrate the effectiveness of the proposed approach.

Sign in / Sign up

Export Citation Format

Share Document