Journal
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
Volume 13, Issue 1, Pages 295-311Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s12652-021-02902-w
Keywords
Fermatean fuzzy sets; Score function; CRITIC; EDAS; Third-party reverse logistics providers
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The paper introduces a hybrid methodology based on Fermate fuzzy sets to solve the S3PRLP selection problem, which can handle unknown attributes and decision makers' weights. The framework combines CRITIC and EDAS methods, demonstrating good performance in a case study and showing its practicality and feasibility.
In today's world, the demand for sustainable third-party reverse logistics providers (S3PRLPs) becomes an increasingly considerable issue for industries seeking improved customer service, cost reduction and sustainability perspectives. However, the assessment and selection of right S3PRLP is a complex uncertain decision-making problem due to involvement of numerous conflicting attributes, imprecise human mind and lack of information. Recently, Fermatean fuzzy set (FFS) has been recognized as one of the suitable tools to tackle the uncertain and inaccurate information. In this paper, we introduce a hybrid methodology based on CRITIC and EDAS methods with Fermatean fuzzy sets (FFSs) to solve the S3PRLP selection problem in which the attributes and decision makers' weights are completely unknown. In this framework, CRITIC approach is applied to calculate the attribute weight and EDAS method is used to evaluate the priority order of S3PRLP options. To do this, a new improved generalized score function (IGSF) is developed with its elegant properties. Also, a formula is discussed to calculate the decision makers' weights based on the developed IGSF. Next, developed framework is applied to assess a case study of S3PRLP selection problem with Fermatean fuzzy information, which elucidates the usefulness and practicality of the proposed method. Finally, comparative study is implemented to show the strength of introduced framework with extant approaches. The outcomes of the work confirm that the introduced approach is more feasible and well-consistent with the other extant approaches.
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