THE ALGORITHM OF COMPETITIVE NORMALIZATION OF CRITERIA IN RATING SYSTEM OF EVALUATION OF THE INTELECTUAL ACHIEVEMENTS
DOI:
https://doi.org/10.51707/2618-0529-2022-23-01Keywords:
ranking alternatives, rating, multiple-criteria decision analysis, domain ontology, taxonomy, information technology.Abstract
In the study and analysis of most processes occurring in any field of human activity, there are applied problems that actually belong to the theory of decision making. The most common of these tasks is to build a ranking list of certain objects (subjects), from which you need to choose the best (worst) in terms of the total value of certain attributes that characterize these objects. The complexity of such tasks is that, as a rule, there are no cases when one or more objects have significant advantages over others in all indicators taken into account. That is why there is a need to apply existing methods of decision theory, as well as to develop algorithms that allow mathematical consideration of the specifics of specific practical problems. The article considers the problem of assessing the achievements of secondary schools in intellectual competitions conducted by the Junior Academy of Sciences of Ukraine and the Ministry of Education and Science, and describes the developed algorithm of competitive normalization of criteria for rating participants. This problem was formalized with the help of ontological methodology, which allowed to implement the algorithm for its solution in the mathematical software TNIAS (Transdisciplinary Network-Centric Information-Analytical System). The developed algorithm is based on the competitive nature of the process of establishing the degree of dominance of some alternatives over others, depending on the analysis of numerical characteristics, which were observed over a period of time. In the general case, the use of the algorithm is most successful in problems when the calculation of rating indicators of alternatives depends not on the absolute numerical values of some criteria, but on the number of alternatives that have close values and do not reach or exceed certain thresholds established by analysis subject area.
References
Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: preferences and value tradeoffs. New York : Wiley.
Ishizaka, A., & Nemery, P. (2013). Multi-criteria decision analysis: methods and software. Chichester : John Wiley & Sons.
Figueira, J., Greco, S., & Ehrgott, M. (2005). Multiple Criteria Decision Analysis: State of the Art Surveys. Boston : Springer Science and Business Media, Inc.
Larichev, O. I. (2003). Teoriya i metody prinyatiya resheniy [Theory and methods of decision making]. Moscow : Logos [in Russian].
Chernorutskiy, I. G. (2005). Metody prinyatiya resheniy [Decision-making methods]. SPb : BKhV-Peterburg [in Russian].
Shtoyer, R. (1992). Mnogokriterialnaya optimizatsiya. Teoriya, vychisleniya i prilozheniya [Multiobjective optimization. Theory, Computing, and applications]. Moscow : Radio i svyaz [in Russian].
Nogin, V. D. (2014). Lineynaya svertka kriteriyev v mnogokriterialnoy optimizatsii [Linear convolution of Criteria in Multi-Criteria Optimization]. Iskusstvennyy intellekt i prinyatiye resheniy – Artificial intelligence and decision making, 4, 73–82 [in Russian].
Lotov, A. V., & Pospelova, I. I. (2008). Mnogokriterialnyye zadachi prinyatiya resheniy [Multicriteria decision-making tasks]. Izdatelskiy otdel f-ta VMiK MGU. MAKS Press [in Russian].
Odu, G. O., & Charles-Owaba, O. E. (2013). Review of Multi-criteria Optimization Methods – Theory and Applications. Journal of Engineering (IOSRJEN), 3 (10), 1–14.
Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156 (2), 445–455. Retrieved from https://doi.org/10.1016/S0377-2217(03)00020-1
Baby, S. (2013). AHP Modeling for Multicriteria Decision-Making and to Optimise Strategies for Protecting Coastal Landscape Resources. International Journal of Innovation, Management and Technology, 4 (2), 218–227.
Yadav, A., Anis, M., Ali, M., & Tuladhar, S. (2015). Analytical Hierarchy Process (AHP) for Analysis: Selection of Passenger Airlines for Gulf Country. International Journal of Scientific & Engineering Research, 6 (3), 379–389.
Kutlu, B., Bozanta, A., Ates, E., Erdogan, S., Gokay, O., K. N. (2014). Project Management Software Selection Using Analytic Hierarchy Process Method. International Journal of Applied Science and Technology, 4 (6), 113–119.
Stryzhak, O., & Kucherov, O. P. (2013). Formuvannia operatsiinoho seredovyshcha informatsiino-analitychnykh system na osnovi ontolohii [Formation of the operational environment of information-analytical systems based on ontologies]. Matematychne modeliuvannia v ekonomitsi – Mathematical modeling in economics, 5, 40–47 [in Ukrainian].
Stryzhak, O. et al. (2021). Decision-making System Based on The Ontology of The Choice Problem. Journal of Physics: Conference Series, 1828 (1), 012007. Retrieved from https://doi.org/10.1088/1742-6596/1828/1/012007
Hwang, C. L., & Yoon, K. (1995). Multiple Attribute Decision Making and Introduction. London : Sage Publication.
Horborukov, V. V., Stryzhak, O. Ye., Franchuk, O. V., & Shapovalov, V. B. (2018). Ontolohichne predstavlennia zadachi ranzhuvannia alternatyv [Ontological representation of the problem of ranking alternatives]. Matematychne modeliuvannia v ekonomitsi – Mathematical modeling in economics, 1 (4), 49–69 [in Ukrainian].
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