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Spherical Fuzzy VIKOR with SWAM and SWGM Operators for MCDM

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Decision Making with Spherical Fuzzy Sets

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 392))

Abstract

In this study, VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), multi-criteria optimization and compromise solution, is extended using spherical fuzzy sets (SFSs). SFSs were recently introduced as a novel type of fuzzy sets that allows the decision-makers to express their degree of hesitation explicitly. Therefore, SFSs provide a greater preference domain for decision-makers. Spherical fuzzy VIKOR (SFVIKOR) is implemented in four different ways based on two aggregation operators and two proposed ideal solutions. The used aggregation operators are the spherical weighted averaging mean (SWAM) and the spherical weighted geometric mean (SWGM). The proposed ideal solutions are the absolute ideal solution and the relative ideal solution. Both ideal solutions don’t rely on the score function and take the degree of hesitation into account. An illustrative example in supplier selection is solved using SFVIKOR with the four implementations and the results are compared with the results of the spherical fuzzy technique of order preference by similarity to an ideal solution (SFTOPSIS). Then, the stability of the solution is examined under these different implementations.

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Authors and Affiliations

  1. Department of Basic Sciences, Higher Technological Institute, Tenth of Ramadan City, Egypt

    Iman Mohamad Sharaf

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  1. Iman Mohamad Sharaf
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Correspondence to Iman Mohamad Sharaf .

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Editors and Affiliations

  1. Department of Industrial Engineering, Faculty of Management, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Turkish Air Force Academy, National Defence University, Istanbul, Turkey

    Fatma Kutlu Gündoğdu

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Sharaf, I.M. (2021). Spherical Fuzzy VIKOR with SWAM and SWGM Operators for MCDM. In: Kahraman, C., Kutlu Gündoğdu, F. (eds) Decision Making with Spherical Fuzzy Sets. Studies in Fuzziness and Soft Computing, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-45461-6_9

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