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Pythagorean fuzzy AHP-TOPSIS integrated approach for transportation management through a new distance measure

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Abstract

This study represents an integrated approach consisting of AHP (analytical hierarchy process) and TOPSIS (technique for order preference by similarity to ideal solution) in Pythagorean fuzzy environment to solve multicriteria decision-making (MCDM) problems with completely unknown weights of criteria. Pythagorean fuzzy numbers are used to capture uncertainties associated with decision makers’ ambiguous judgment for the selection of transportation companies in an MCDM context which can also be considered as an important area of supply chain management. In the proposed approach, a new distance measure based on cross-evaluation of Pythagorean fuzzy sets is introduced to overcome the disadvantages of existing distance measures. Unlike existing MCDM approaches, in which either subjective or objective weights of criteria are used, in this method AHP is modified using Pythagorean fuzzy information to calculate subjective weights of criteria, as well as Pythagorean fuzzy entropy weight model is applied to compute objective weights of criteria. Finally, both the weights of criteria are simultaneously taken into consideration to obtain final ranking of the alternatives. To demonstrate the application potentiality of the proposed method, a numerical example is considered with Pythagorean fuzzy input in the context of selecting the best transportation company which is one of the most important parts of transportation management. Further, a comparative analysis is executed with other existing techniques to establish the efficacy of the proposed methodology.

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Correspondence to Animesh Biswas.

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Sarkar, B., Biswas, A. Pythagorean fuzzy AHP-TOPSIS integrated approach for transportation management through a new distance measure. Soft Comput 25, 4073–4089 (2021). https://doi.org/10.1007/s00500-020-05433-2

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