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Entropy–KEMIRA Approach for MCDM Problem Solution in Human Resources Selection Task

Author

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  • Aleksandras Krylovas

    (Department of Mathematical Modelling, Vilnius Gediminas Technical University, Sauletekio av. 11, Vilnius, Lithuania)

  • Stanislavas Dadelo

    (Department of Philosophy and Communication, Vilnius Gediminas Technical University, Sauletekio av. 11, Vilnius, Lithuania)

  • Natalja Kosareva

    (Department of Mathematical Modelling, Vilnius Gediminas Technical University, Sauletekio av. 11, Vilnius, Lithuania)

  • Edmundas Kazimieras Zavadskas

    (Department of Construction Technology and Management, Vilnius Gediminas Technical University, Sauletekio av. 11, Vilnius, Lithuania)

Abstract

Entropy–KEMIRA approach is proposed for criteria ranking and weights determining when solving Multiple Criteria Decision-Making (MCDM) problem in human resources selection task. For the first time the method is applied in the case of three groups of criteria. Weights are calculated by solving optimization problem of maximizing the number of elements, which are “best” according to all three criteria, and minimizing the number of “doubtful” elements. The algorithm of problem solution is presented in the paper. The numerical experiment with three groups of evaluation criteria describing 11 life goals was accomplished.

Suggested Citation

  • Aleksandras Krylovas & Stanislavas Dadelo & Natalja Kosareva & Edmundas Kazimieras Zavadskas, 2017. "Entropy–KEMIRA Approach for MCDM Problem Solution in Human Resources Selection Task," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(05), pages 1183-1209, September.
    • Handle: RePEc:wsi:ijitdm:v:16:y:2017:i:05:n:s0219622017500274
    DOI: 10.1142/S0219622017500274
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    References listed on IDEAS

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    1. Stanislav Dadelo, 2015. "Students' Personal Life Goals Assessed Using Settings Weighting Method," Managing Intellectual Capital and Innovation for Sustainable and Inclusive Society: Managing Intellectual Capital and Innovation; Proceedings of the MakeLearn and TIIM Joint International Conference 2,, ToKnowPress.
    2. Aleksandras Krylovas & Edmundas Kazimieras Zavadskas & Natalja Kosareva & Stanislav Dadelo, 2014. "New KEMIRA Method for Determining Criteria Priority and Weights in Solving MCDM Problem," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(06), pages 1119-1133.
    3. Edmundas Kazimieras Zavadskas & Valentinas Podvezko, 2016. "Integrated Determination of Objective Criteria Weights in MCDM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 267-283, March.
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    Cited by:

    1. Ruta Dadeliene & Stanislav Dadelo & Natalija Pozniak & Leonidas Sakalauskas, 2020. "Analysis of top kayakers’ training-intensity distribution and physiological adaptation based on structural modelling," Annals of Operations Research, Springer, vol. 289(2), pages 195-210, June.
    2. Aleksandras Krylovas & Natalja Kosareva & Stanislav Dadelo, 2020. "European Countries Ranking and Clustering Solution by Children’s Physical Activity and Human Development Index Using Entropy-Based Methods," Mathematics, MDPI, vol. 8(10), pages 1-22, October.
    3. Aleksandras Krylovas & Rūta Dadelienė & Natalja Kosareva & Stanislav Dadelo, 2019. "Comparative Evaluation and Ranking of the European Countries Based on the Interdependence between Human Development and Internal Security Indicators," Mathematics, MDPI, vol. 7(3), pages 1-18, March.
    4. Pelin Toktaş & Gülin Feryal Can, 2019. "Stochastic KEMIRA-M Approach with Consistent Weightings," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 793-831, May.
    5. Aleksandras Krylovas & Natalja Kosareva & Rūta Dadelienė & Stanislav Dadelo, 2020. "Evaluation of Elite Athletes Training Management Efficiency Based on Multiple Criteria Measure of Conditioning Using Fewer Data," Mathematics, MDPI, vol. 8(1), pages 1-17, January.

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