scholarly journals Models for Green Supplier Selection in Green Supply Chain Management With Pythagorean 2-Tuple Linguistic Information

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 18042-18060 ◽  
Author(s):  
Xiyue Tang ◽  
Guiwu Wei
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supplier Selection ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Linguistic Information ◽  
Chain Management ◽  
Green Supplier Selection

scholarly journals Models for MADM with Single-Valued Neutrosophic 2-Tuple Linguistic Muirhead Mean Operators

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 442 ◽  
Author(s):  
Jie Wang ◽  
Jianping Lu ◽  
Guiwu Wei ◽  
Rui Lin ◽  
Cun Wei
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Supplier Selection ◽  
Multiple Attribute Decision Making ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Chain Management ◽  
Green Supplier Selection ◽  
Multiple Attribute

In this article, we expand the Muirhead mean (MM) operator and dual Muirhead mean (DMM) operator with single-valued neutrosophic 2-tuple linguistic numbers (SVN2TLNs) to propose the single-valued neutrosophic 2-tuple linguistic Muirhead mean (SVN2TLMM) operator, the single-valued neutrosophic 2-tuple linguistic weighted Muirhead mean (SVN2TLWMM) operator, the single-valued neutrosophic 2-tuple linguistic dual Muirhead mean (SVN2TLDMM) operator, and the single-valued neutrosophic 2-tuple linguistic weighted dual Muirhead mean (SVN2TLWDMM) operator. Multiple attribute decision making (MADM) methods are then proposed using these operators. Finally, we utilize an applicable example for green supplier selection in green supply chain management to prove the proposed methods.

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 Driving green supply chain management performance through supplier selection and value internalisation

2017 ◽  
Vol 37 (4) ◽  
pp. 489-509 ◽  
Author(s):  
Jens K. Roehrich ◽  
Stefan U. Hoejmose ◽  
Victoria Overland
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supplier Selection ◽  
Secondary Data ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Structured Interviews ◽  
Chain Management ◽  
Content Type ◽  
Second Tier

Purpose The purpose of this paper is to apply self-determination theory (SDT) to green supply chain management (GSCM) and explore how green supplier selection (GSS) drives GSCM performance and how realisation of improved GSCM performance is contingent upon SDT mechanisms of autonomy, competence and relatedness. Design/methodology/approach This study draws on 18 semi-structured interviews and secondary data from a Germany-based first-tier aircraft interior manufacturer and its six key suppliers. The focal company was selected because it is recognised as having achieved high GSCM standards in the aerospace industry. Findings The study draws out the importance of GSS, distinguishing between new and legacy suppliers, and offers significant insights into how suppliers’ motivation and downstream GSCM criteria can be internalised in second-tier suppliers to drive GSCM performance. Practical implications GSS should be considered not only for new suppliers but also at an ongoing basis for legacy suppliers. Focal companies must realise the importance of motivating supply chain partners to realise GSCM practices and need to first build-up autonomy before focussing on competence and relatedness sub-dimensions. Originality/value The authors make a significant contribution to the GSCM literature by conducting a study of first-tier-second-tier relationships, thus moving beyond the buyer-supplier relationships investigated in extant studies. The results theoretically and empirically draw out key factors in GSS and supplier motivation in engaging with GSCM practices, thus driving GSCM performance.

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