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Decision analysis with classic and fuzzy EDAS modifications

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Abstract

In this paper, we introduce L1 metrics in evaluation based on distance from average solution method for multi-criteria decision making. The strength of the proposed modification stems from the following advantages brought by its new distance measures: (1) capability for working with varied statistical data types; (2) increased sensitivity when comparing values of similar magnitudes; and (3) minimized influence of large differences between elements. We also present a variant of this algorithm that is suitable for trapezoidal fuzzy numbers. The merit of the new fuzzy modification is reduced time complexity due to the proposed calculation simplifications. The effectiveness and practicality of these new extensions are illustrated by three data sets for the best alternative selection. The results show that the modifications produce equal or very similar ranking in comparison with original algorithm and other well-known multi-criteria decision-making methods.

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Acknowledgements

This research was supported by the Scientific Research Fund of the University of Plovdiv Paisii Hilendarski as a part of Project SR17 FESS 012/25.04.2017.

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Authors and Affiliations

  1. Faculty of Economics and Social Sciences, University of Plovdiv Paisii Hilendarski, 24 Tzar Asen, 4000, Plovdiv, Bulgaria

    Galina Ilieva, Tania Yankova & Stanislava Klisarova-Belcheva

Authors
  1. Galina Ilieva
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  2. Tania Yankova
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  3. Stanislava Klisarova-Belcheva
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Corresponding author

Correspondence to Galina Ilieva.

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The authors declare that they have no conflict of interest.

Additional information

Communicated by Anibal Tavares de Azevedo.

Appendix

Appendix

See Tables 6, 7, 8, 9, 10, 11, 12, 13 and Figs. 1, 2.

Table 6 Data of the inventory classification problem and average solutions (Keshavarz Ghorabaee et al. 2015)
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Table 7 Data of the inventory classification problem and average solutions after linear normalization (Keshavarz Ghorabaee et al. 2015)
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Table 8 Results of the L1 Manhattan EDAS modification
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Table 9 Results of ABC classification with different L1 metrics
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Table 10 Data of the MCDM Example 2 for comparative analysis (Keshavarz Ghorabaee et al. 2015)
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Table 11 Normalized weights of criteria in different sets (Keshavarz Ghorabaee et al. 2015)
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Table 12 Results of ranking with EDAS, new EDAS modification and four MCDM methods
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Table 13 L1 Manhattan EDAS modification via fuzzy trapezoidal numbers—step-by-step calculations
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Ilieva, G., Yankova, T. & Klisarova-Belcheva, S. Decision analysis with classic and fuzzy EDAS modifications. Comp. Appl. Math. 37, 5650–5680 (2018). https://doi.org/10.1007/s40314-018-0652-0

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