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Determining the Main Resilience Competencies by Applying Fuzzy Logic in Military Organization

Author

Listed:
  • Svajone Bekesiene

    (General Jonas Zemaitis Military Academy of Lithuania, Silo 5a, LT-10322 Vilnius, Lithuania)

  • Oleksandr Nakonechnyi

    (Taras Shevchenko National University of Kyiv, Volodymyrska St, 64/13, 01601 Kyiv, Ukraine)

  • Olena Kapustyan

    (Taras Shevchenko National University of Kyiv, Volodymyrska St, 64/13, 01601 Kyiv, Ukraine)

  • Rasa Smaliukiene

    (General Jonas Zemaitis Military Academy of Lithuania, Silo 5a, LT-10322 Vilnius, Lithuania)

  • Ramutė Vaičaitienė

    (General Jonas Zemaitis Military Academy of Lithuania, Silo 5a, LT-10322 Vilnius, Lithuania)

  • Dalia Bagdžiūnienė

    (General Jonas Zemaitis Military Academy of Lithuania, Silo 5a, LT-10322 Vilnius, Lithuania)

  • Rosita Kanapeckaitė

    (General Jonas Zemaitis Military Academy of Lithuania, Silo 5a, LT-10322 Vilnius, Lithuania)

Abstract

Military training programs have been developed to enhance soldier resilience competencies, which are necessary for soldiers to perform their duties effectively under stress. The ongoing military conflict in Ukraine and the experience of previous military missions abroad emphasize the need for effective training that helps soldiers recover quickly and continue their missions. However, selecting the most suitable resilience training program is challenging and the selection criteria need to be optimized to ensure the most needed competencies are considered. This study aimed to utilize a fuzzy MCDM method to establish the priority weight of decision-making criteria, identifying the core competencies necessary for soldier resilience training, and utilizing the fuzzy TOPSIS method to rank and select the most appropriate training program. The evaluation results were calculated using the MATLAB (R2020b) mathematical package developed by MathWorks. The application of the hierarchical MCDA model based on fuzzy sets theory indicated that mental agility is the most important competence in high-stress environments. The study found that the Mindfulness-Based Mind Fitness Training (MMFT) program, which is intended to regulate soldiers’ emotions, had the highest rank among evaluated options according to the combined FAHP sub-factor fuzzy weights and alternatives evaluation conducted using FTOPSIS. The study provides valuable information on the selection of military resilience training programs.

Suggested Citation

  • Svajone Bekesiene & Oleksandr Nakonechnyi & Olena Kapustyan & Rasa Smaliukiene & Ramutė Vaičaitienė & Dalia Bagdžiūnienė & Rosita Kanapeckaitė, 2023. "Determining the Main Resilience Competencies by Applying Fuzzy Logic in Military Organization," Mathematics, MDPI, vol. 11(10), pages 1-23, May.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2270-:d:1145761
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    References listed on IDEAS

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    1. Neha Ghorui & Arijit Ghosh & Ebrahem A. Algehyne & Sankar Prasad Mondal & Apu Kumar Saha, 2020. "AHP-TOPSIS Inspired Shopping Mall Site Selection Problem with Fuzzy Data," Mathematics, MDPI, vol. 8(8), pages 1-22, August.
    2. Chan, Felix T.S. & Kumar, Niraj, 2007. "Global supplier development considering risk factors using fuzzy extended AHP-based approach," Omega, Elsevier, vol. 35(4), pages 417-431, August.
    3. Sangeeta Pant & Anuj Kumar & Mangey Ram & Yury Klochkov & Hitesh Kumar Sharma, 2022. "Consistency Indices in Analytic Hierarchy Process: A Review," Mathematics, MDPI, vol. 10(8), pages 1-15, April.
    4. Rasa Smaliukienė & Svajone Bekesiene & Asta Mažeikienė & Gerry Larsson & Dovilė Karčiauskaitė & Eglė Mazgelytė & Ramutė Vaičaitienė, 2022. "Hair Cortisol, Perceived Stress, and the Effect of Group Dynamics: A Longitudinal Study of Young Men during Compulsory Military Training in Lithuania," IJERPH, MDPI, vol. 19(3), pages 1-15, January.
    5. Yiming Jiang & Chenguang Yang & Hongbin Ma, 2016. "A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-11, March.
    6. Chang, Da-Yong, 1996. "Applications of the extent analysis method on fuzzy AHP," European Journal of Operational Research, Elsevier, vol. 95(3), pages 649-655, December.
    7. Kajal Chatterjee & Samarjit Kar, 2016. "Multi-criteria analysis of supply chain risk management using interval valued fuzzy TOPSIS," OPSEARCH, Springer;Operational Research Society of India, vol. 53(3), pages 474-499, September.
    8. Zhu, Ke-Jun & Jing, Yu & Chang, Da-Yong, 1999. "A discussion on Extent Analysis Method and applications of fuzzy AHP," European Journal of Operational Research, Elsevier, vol. 116(2), pages 450-456, July.
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