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An Extended QUALIFLEX Method with Comprehensive Weight for Green Supplier Selection in Normal q-Rung Orthopair Fuzzy Environment

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Abstract

Green supplier selection (GSS) plays an irreplaceable role in green supply chain management and environmental protection. Simultaneously, it is widely studied as a typical multi-attribute decision-making (MADM) problem. In this paper, the qualitative flexible multiple criteria (QUALIFLEX) method is used to solve the problem of green suppliers which the number of alternatives is far smaller than the number of attributes. Firstly, the normal q-rung orthopair fuzzy numbers (q-RONFNs) is proposed to overcome the limitation of normal intuitionistic fuzzy numbers (NIFNs) that it cannot handle situation where the sum of the membership degree and non-membership degree is greater than or equal to 1. Secondly, we adopt the combination weight which takes into account subjective and objective weights in MADM, where the subjective weight and objective weight are calculated by fuzzy best–worst method (FBWM) and q-RONF criteria importance through inter-criteria correlation (q-RONFNs-CRITIC) method, respectively, and the combination weight is calculated based on the maximum comprehensive evaluation values. Furthermore, based on QUALIFLEX as the decision-making framework, the q-RONFNs-QUALIFLEX method for GSS is proposed. Finally, this model is applied to the GSS, and the effectiveness and superiority of this model are proved by the comparison with the existing methods.

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China (No. 71771140), Project of cultural masters and “the four kinds of a batch” talents, the Special Funds of Taishan Scholars Project of Shandong Province (No. ts201511045), and Major bidding projects of National Social Science Fund of China (19ZDA080).

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Correspondence to Peide Liu.

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Liu, P., Pan, Q., Xu, H. et al. An Extended QUALIFLEX Method with Comprehensive Weight for Green Supplier Selection in Normal q-Rung Orthopair Fuzzy Environment. Int. J. Fuzzy Syst. 24, 2174–2202 (2022). https://doi.org/10.1007/s40815-021-01234-3

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