Russian Federation
UDC 519.816
Modern organizational systems operate in conditions of high complexity and uncertainty, which makes the problem of multi-criteria selection particularly relevant. The article proposes an algorithm and software for multi-criteria assessment and selection of solutions under uncertainty using the apparatus of fuzzy logic of the first and second order. The application of the developed algorithm and software is considered using the example of solving the problem of personnel selection, as one of the key ones for organizational management. The approach proposed in the article allows taking into account both objective and subjective expert data expressed in various scales, including linguistic ones. The novelty of the study lies in the integration of second-order fuzzy logic methods, which allows modeling the meta-uncertainty of expert assessments, which provides more stable and objective results compared to traditional methods.
multicriteria assessment, decision making, organizational systems, first-order fuzzy logic, second-order fuzzy logic, algorithm, personnel selection, uncertainty, linguistic scale
1. Kalach A.V., Tolmasov R.S., Smolenceva T.E. Upravlenie sostavom v organizacionnyh sistemah // Vestnik Voronezhskogo instituta MVD Rossii. 2023. № 3. S. 59–69. EDN PGCFRA.
2. Metod mnogokriterial'noj ocenki effektivnosti tekhnicheskih sredstv v organizacionno-tekhnicheskih sistemah / D.F. Kozhevin [i dr.] // Nauch.-analit. zhurn. «Vestnik S.-Peterb. un-ta GPS MCHS Rossii». 2023. № 4. S. 59–70. DOI:https://doi.org/10.61260/2218-130X-2024-2023-4-59-70. EDN NWHMEP.
3. L'vovich Ya.E., Shevyreva E.A. Optimizaciya upravleniya v organizacionnoj sisteme s al'ternativnymi postavkami na osnove modeli i algoritma mnogovariantnogo vybora // Modelirovanie, optimizaciya i informacionnye tekhnologii. 2024. T. 12. № 4 (47). DOI:https://doi.org/10.26102/2310-6018/2024.47.4.018. EDN LOHJUZ.
4. Aruldoss M., Lakshmi T.M., Venkatesan V.P. A survey on multi criteria decision making methods and its applications // American Journal of Information Systems. 2013. Vol. 1. № 1. P. 31–43. DOI:https://doi.org/10.12691/ajis-1-1-5.
5. Thakkar J.J. Multi-criteria decision making // Singapore: Springer. 2021. Vol. 336. P. 1–365. DOI:https://doi.org/10.1007/978-981-33-4745-8.
6. Munier N., Hontoria E. Uses and Limitations of the AHP Method, 2021. DOI:https://doi.org/10.1007/978-3-030-60392-2.
7. Nechetkaya mnogokriterial'naya sistema podderzhki prinyatiya reshenij DecernsFMCDA / B.I. Yacalo [i dr.] // Programmnye produkty i sistemy. 2022. № 2. S. 171–183. DOI:https://doi.org/10.15827/0236-235X.138.171-183. EDN VGDICX.
8. Methodology for integrated multicriteria decision-making with uncertainty: Extending the compromise ranking method for uncertain evaluation of alternatives / M. Juanpera [et al.] // Fuzzy Sets and Systems. 2022. Vol. 434. P. 135–158. DOI:https://doi.org/10.1016/j.fss.2021.08.008.
9. Techniques to model uncertain input data of multi‐criteria decision‐making problems: a literature review / R. Pelissari [et al.] // International Transactions in Operational Research. 2021. Vol. 28. № 2. P. 523–559. DOI:https://doi.org/10.1111/itor.12598.
10. Chechnev V.B. Analiz i klassifikaciya mnogokriterial'nyh metodov prinyatiya reshenij // Ontologiya proektirovaniya. 2024. T. 14. № 4 (54). S. 607–624. DOI:https://doi.org/10.18287/2223-9537-2024-14-4-607-624. EDN QMCAUL.
11. A comparative analysis of Multi-Criteria Decision-Making methods and normalization techniques in holistic sustainability assessment for engineering applications/ S. Malefaki [et al.] // Aerospace. 2025. Vol. 12. № 2. P. 100. DOI:https://doi.org/10.3390/aerospace12020100.
12. Podinovskij V.V., Potapov M.A. Metod vzveshennoj summy kriteriev v analize mnogokriterial'nyh reshenij: Pro et contra // Biznes-informatika. 2013. № 3 (25). S. 41–48. EDN RBWNYH.
13. Podinovskaya O.V. Metod analiza ierarhij kak metod podderzhki prinyatiya mnogokriterial'nyh reshenij // Informacionnye tekhnologii modelirovaniya i upravleniya. 2010. № 1 (60). S. 71–80. EDN KZJDNT.
14. Seredkin K.A. O granicah primenimosti metoda analiticheskih setej v zadachah prinyatiya reshenij v estestvennyh naukah // Iskusstvennyj intellekt i prinyatie reshenij. 2018. № 2. S. 95–102. DOI:https://doi.org/10.14357/20718594180208. EDN USBLEW.
15. Accounting for uncertainty in value judgements when applying multi-attribute value theory / R.A. Estévez [et al.] // Environmental Modeling & Assessment. 2018. Vol. 23. №. 1. P. 87–97. DOI:https://doi.org/10.1007/s10666-017-9555-5.
16. Kravchenko T.K., Druzhaev A.A. Adaptaciya metodov semejstva ELECTRE dlya vklyucheniya v Ekspertnuyu sistemu podderzhki prinyatiya reshenij // Biznes-informatika. 2015. № 2 (32). S. 69–78. EDN UBMTPR.
17. Kravchenko T.K., Avdeev Yu.V. Razvitie sistem podderzhki prinyatiya reshenij s ispol'zovaniem metoda PROMETHEE // Aktual'nye problemy gumanitarnyh i estestvennyh nauk. 2010. № 9. S. 67–70. EDN NDKCTR.
18. PROMETHEE: A comprehensive literature review on methodologies and applications / M. Behzadian [et al.] // European journal of Operational research. 2010. Vol. 200. № 1. P. 198–215. DOI:https://doi.org/10.1016/j.ejor.2009.01.021.
19. Halickaya K. Vybor tekhnologij s pomoshch'yu metoda TOPSIS // Forsajt. 2020. T. 14. № 1. S. 85–96. DOI:https://doi.org/10.17323/2500-2597.2020.1.85.96. EDN GYSKAB.
20. Kelemenis A., Askounis D. A new TOPSIS-based multi-criteria approach to personnel selection // Expert systems with applications. 2010. Vol. 37. № 7. P. 4999–5008. DOI:https://doi.org/10.1016/j.eswa.2009.12.013.
21. Greco S., Ehrgott M., Figueira J.R. Multiple criteria decision analysis. NY: Springer, 2016. P. 43–95. DOI:https://doi.org/10.1007/978-1-4939-3094-4_3.
22. Uchet neopredelennostej v ramkah mnogokriterial'nogo analiza reshenij s ispol'zovaniem koncepcii priemlemosti / B.I. Yacalo [i dr.] // Upravlenie bol'shimi sistemami: sb. trudov. 2011. № 32. S. 5–30. EDN NQVPAT.
23. Lahdelma R., Salminen P. Stochastic multicriteria acceptability analysis (SMAA) // Trends in multiple criteria decision analysis. 2010. P. 285–315. DOI:https://doi.org/10.1007/978-1-4419-5904-1_10.
24. A review and classification of approaches for dealing with uncertainty in multi-criteria decision analysis for healthcare decisions / H. Broekhuizen [et al.] // Pharmacoeconomics. 2015. Vol. 33. № 5. P. 445–455. DOI:https://doi.org/10.1007/s40273-014-0251-x.
25. Vilkov V.B., Chernyh A.K., Flegontov A.V. Teoriya i praktika optimizacii reshenij na osnove nechetkih mnozhestv i nechyotkoj logiki: monografiya. SPb: RGPU im. A.I. Gercena, 2017. 159 s. ISBN 978-5-8064-2342-0. EDN YKYFLZ.
26. A comprehensive review on type 2 fuzzy logic applications: Past, present and future / K. Mittal [et al.] // Engineering Applications of Artificial Intelligence. 2020. Vol. 95. P. 103916. DOI:https://doi.org/10.1016/j.engappai.2020.103916.
27. Gerasimova L.V., Pogozhev I.B. Kompleksnaya ocenka kachestva proektov i vybor optimal'nogo varianta po metodu akademika A.N. Krylova // Standarty i kachestvo. 1972. № 8. S. 37–39.
28. Magrez P., Smets P. Fuzzy modus ponens: A new model suitable for applications in knowledge-based systems // Readings in Fuzzy Sets for Intelligent Systems. Morgan Kaufmann. 1993. P. 565–574. DOI:https://doi.org/10.1016/B978-1-4832-1450-4.50060-2.
29. Remezova E.M. Nechetkie mnozhestva vtorogo poryadka: ponyatie, analiz i osobennosti primeneniya // Sovremennye problemy nauki i obrazovaniya. 2013. № 5. S. 435. EDN RRJWEZ.
30. Utkin O.V. Ispol'zovanie nechetkoj logiki v kompleksnoj modeli ocenki kandidatov, postupayushchih v obrazovatel'nuyu organizaciyu MChS Rossii // Vestnik Voronezhskogo instituta FSIN Rossii. 2021. № 3. S. 131–142. EDN ETYHKG.
31. Utkin O.V. Otbor kandidatov v magistraturu obrazovatel'nyh organizacij MCHS Rossii s ispol'zovaniem adaptivnoj metodiki ocenki na osnove apparata nechetkoj logiki // Nauch.-analit. zhurn. «Vestnik S.-Peterb. un-ta GPS MCHS Rossii». 2021. № 3. S. 194–203. EDN JCXIXX.
32. Avtomatizirovannaya sistema nechetkogo vyvoda: svidetel'stvo o gosudarstvennoj registracii programmy dlya EVM RU № 2016661045, 28.09.2016. Zayavka ot 04.08.2016.
