Development of a technology of structuring group expert judgments under various types of uncertainty

Authors

DOI:

https://doi.org/10.15587/1729-4061.2018.133299

Keywords:

expert preferences, aggregation

Abstract

The study considers the problem of structuring expert judgments formed under conditions of uncertainty of different nature and in presence of conflicting expert evidence. The method of aggregating group expert judgments that are formed under conditions of various types of uncertainty helps synthesize the group opinion, taking into account various forms of representing the preferences of experts (interval, fuzzy and crisp expert judgments). The proposed procedure makes it possible to synthesize a group decision in the event that there is a group or several groups of experts in a group of experts who express their preferences using different forms of expert judgments.

This approach allows reflecting accurately the expert preferences regarding the object being analysed, without restricting the experts to a rigid form of presenting assessments.

In order to analyse the obtained expert information and to get individual expert ratings of the analysed objects, the method of pairwise comparison and its modification were used in the study.

It has been established that for the aggregation of crisp expert estimates, more precise combined results can be obtained by applying rules for redistributing conflicts of the theory of plausible and paradoxical reasoning. To aggregate interval expert judgments, one of the combination rules of the theory of evidence is recommended. It has been determined that in order to improve the quality of the aggregate results, it is advisable to establish a procedure for combining expert inputs, for example, taking into account the degree of dissimilarity and the structure of expert evidence.

The obtained results are intended to help improve the quality and efficiency of the processes of preparing and making decisions while solving the problems of analysing and structuring expert judgments.

Author Biographies

Igor Kovalenko, Petro Mohyla Black Sea National University 68 Desantnykiv str., 10, Mykolaiv, Ukraine, 54003

Doctor of Technical Sciences, Professor

Department of software engineering

Alyona Shved, Petro Mohyla Black Sea National University 68 Desantnykiv str., 10, Mykolaiv, Ukraine, 54003

PhD

Department of software engineering

References

  1. Nedashkovskaya, N. I., Pankratova, N. D. (2007). Metodologiya obrabotki nechetkoy ekspertnoy informacii v zadachah predvideniya. Ch. 1. Problemy upravleniya i informatiki, 2, 40–55.
  2. Prabjot, K., Mahanti, N. C. (2008). A fuzzy ANP-based approach for selection ERP vendors. International Journal of Soft Computing, 3 (1), 24–32.
  3. Chen, T.-Y., Ku, T.-C. (2008). Importance-assessing method with fuzzy number-valued fuzzy measures and discussions on TFNs and TrFNs. International Journal of Fuzzy Systems, 10 (2), 92–103.
  4. Dubrovin, V. I., Mironova, N. A. (2009). Metod polucheniya vektora prioritetov iz nechetkih matric poparnyh sravneniy. Iskusstvenniy intellekt, 3, 464–470.
  5. Makui, A., Fathi, M., Narenji, M. (2010). Interval Weighted Comparison Matrices – A Review. International Journal of Industrial Engineering & Production Research, 20 (4), 139–156.
  6. Sayko, V. V. (2009). Modifikaciya metoda parnyh sravneniy dlya sluchaev s bol'shim kolichestvom ocenivaemyh parametrov. Intellektual'nye sistemy prinyatiya resheniy i problemy vychislitel'nogo intellekta (ISDMCI’ 2009): Materialy mezhdunar. nauch. konf. Vol. 1. Evpatoriya, 210–214.
  7. Pavlov, O. A., Lishchuk, K. I., Shtankevych, O. S., Ivanova, H. A., Fedotov, O. P. (2010). Modyfikovanyi metod analizu ierarkhiy (versiya 1,2). Visnyk NTUU «KPI». Seriya: Informatyka, upravlinnia ta obchysliuvalna tekhnika, 50, 43–54.
  8. Beynon, M. J. (2014). Reflections on DS/AHP: Lessons to Be Learnt. Lecture Notes in Computer Science, 95–104. doi: 10.1007/978-3-319-11191-9_11
  9. Stopchenko, H. I., Makrushan, I. A., Bilan, S. V. (2010). Zadachi ta kontseptsiyi metodiv bahatokryterialnykh rishen v intelektualnykh systemakh. Bionika intelektu, 1 (72), 122–125.
  10. Krejci, J. (2018). Pairwise comparison matrices and their fuzzy extension: multi-criteria decision making with a new fuzzy approach. Vol. 366. Fuzziness and Soft Computing. Springer International Publishing, 288. doi: 10.1007/978-3-319-77715-3
  11. Bulut, E., Duru, O., Keçeci, T., Yoshida, S. (2012). Use of consistency index, expert prioritization and direct numerical inputs for generic fuzzy-AHP modeling: A process model for shipping asset management. Expert Systems with Applications, 39 (2), 1911–1923. doi: 10.1016/j.eswa.2011.08.056
  12. Dopazo, E., Lui, K., Chouinard, S., Guisse, J. (2014). A parametric model for determining consensus priority vectors from fuzzy comparison matrices. Fuzzy Sets and Systems, 246, 49–61. doi: 10.1016/j.fss.2013.07.022
  13. Demirel, T., Demirel, N. Ç., Kahraman, C. (2008). Fuzzy Analytic Hierarchy Process and its Application. Fuzzy Multi-Criteria Decision Making, 53–83. doi: 10.1007/978-0-387-76813-7_3
  14. Nedashkovskaya, N. I. (2015). Modeli parnyh sravneniy na osnovanii interval'nyh ocenok ekspertov. Pytannia prykladnoi matematyky i matematychnoho modeliuvannia. 2015. Available at: http://pm-mm.dp.ua/index.php/pmmm/article/view/112/112
  15. Wang, Y.-M., Elhag, T. M. S. (2007). A goal programming method for obtaining interval weights from an interval comparison matrix. European Journal of Operational Research, 177 (1), 458–471. doi: 10.1016/j.ejor.2005.10.066
  16. Smarandache, F., Dezert, J., Tacnet, J. (2010). Fusion of sources of evidence with different importances and reliabilities. 2010 13th International Conference on Information Fusion. doi: 10.1109/icif.2010.5712071
  17. Uzhga-Rebrov, O. I. (2010). Upravlenie neopredelennostyami. Ch. 3. Sovremennye neveroyatnostnye metody. Rezekne: Izdevnieciba, 560.
  18. Fu, C., Yang, S. (2012). An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements. European Journal of Operational Research, 223 (1), 167–176. doi: 10.1016/j.ejor.2012.05.048
  19. Yan, Y., Suo, B. (2013). A Novel D-S Combination Method for Interval-valued Evidences. Research Journal of Applied Sciences, Engineering and Technology, 6 (13), 2326–2331. doi: 10.19026/rjaset.6.3703
  20. Jousselme, A.-L., Maupin, P. (2012). Distances in evidence theory: Comprehensive survey and generalizations. International Journal of Approximate Reasoning, 53 (2), 118–145. doi: 10.1016/j.ijar.2011.07.006
  21. Shved, A., Davydenko, Y. (2016). The analysis of uncertainty measures with various types of evidence. 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP). doi: 10.1109/dsmp.2016.7583508
  22. Antonucci, A. (2012). An Interval-Valued Dissimilarity Measure for Belief Functions Based on Credal Semantics. Belief Functions: Theory and Applications, 37–44. doi: 10.1007/978-3-642-29461-7_4
  23. Kovalenko, I. I., Shved, A. V. (2016). Clustering of group expert estimates based on measures in the theory of evidence. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 4, 71–77.

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Published

2018-06-11

How to Cite

Kovalenko, I., & Shved, A. (2018). Development of a technology of structuring group expert judgments under various types of uncertainty. Eastern-European Journal of Enterprise Technologies, 3(4 (93), 60–68. https://doi.org/10.15587/1729-4061.2018.133299

Issue

Vol. 3 No. 4 (93) (2018): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2018 Igor Kovalenko, Alyona Shved

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