ELECTRE methodology and automation of alternative ranking
https://doi.org/10.26425/2658-3445-2022-5-3-26-37
Abstract
The authors consider the stages of applying the ELECTRE methodology: multi-criteria ranking of alternatives to. This approach assumes taking into account the values of the features characterizing the evaluated alternatives and the significance levels of these features. Based on these expert assessments, the indices of agreement and disagreement are calculated. Their values determine the validity of the assumption of the superiority of alternatives over each other. The implementation of strictly defined stages of the methodology makes it possible to identify the order of dominance of alternatives and rank them in accordance with the priorities of the decision-maker. The algorithms of ELECTRE family offers a deterministic approach to decision-making, in which methods and models are considered as practical means of tactical and strategic analysis of the situation. Automated decision support systems implementing these methods make it possible to document an understanding of the actual preferences of decision makers. The ELECTRE I method uses verbal statements stating binary relationships between alternative strategies. Binary relations are expressed by the indices of agreement and disagreement. The results obtained indicate effectiveness of the methodology under study.
About the Authors
P. V. TerelianskyRussian Federation
Pavel V. Tereliansky - Dr. Sci. (Econ.), Cand. Sci. (Tech.), Deputy Director of the Center for the Implementation of projects for the development of the University third mission
Moscow
S. Yu. Kuznetsov
Russian Federation
Sergey Yu. Kuznetsov - Cand. Sci. (Econ.), Assoc. Prof. at the Information system in Economics Department
Volgograd
References
1. Azgaldov G.G. (1982), Theory and practice of assessing the quality of goods. Fundamentals of Qualimetry, Economics, Moscow, USSR (in Russian).
2. Azgaldov G.G. (1989), Qualimetry in architectural and construction design, Strojizdat, Moscow, USSR (in Russian).
3. Azgaldov G.G., Raikhman E.P. (1973), About Qualimetry, Publishing house of standards, Moscow, USSR (in Russian).
4. Bragina E.I. (2012), “The history of the development of neural networks and their prospects”, In: Modern society, education and science: Proceedings of the International scientific-practical conference in 3 parts, Tambov 25 June 2012, part 3, Yucom Consulting Company LLC, Tambov, Russia, pp. 18–19.
5. Gagarin A.G., Kostikova A.V. (2017), “Methodical approaches to computer modeling of complex systems using fuzzy logic”, In: Ovchinnikov A.S. (ed.) Ecological and reclamation aspects of rational nature management: Proceedings of International scientific practical conference, Volgograd 31 Janyary–3 February 2017, Volgograd State Agrarian University Publ. House, Volgograd, Russia, vol. 3, pp. 442–447.
6. Dekatov D.E., Andreichikov A.V., Andreichikova O.N. (2008), “Multi-criteria assessment of the competitiveness of innovative organizations by automated methods of the ELECTRE family”, Innovation Management, no. 3, pp. 180–186.
7. Dekatov D.E. (2010), “Forecasting and analysis of the competitiveness of organizations based on the procedures of decision theory methods”, In: Economic forecasting: models and methods: Proceedings of VI International scientific practical conference, Voronezh, 6 April 2010, in 2 parts, part 1, Voronezh state university Publ. House, Voronezh, Russia, pp. 125–130.
8. Ferreira Opaso E.V. (2012), “Modern interactive interfaces”, In: Theoretical and practical aspects of the development of modern science: Proceedings of the V International scientific practical conference, Moscow, 2–3 October 2012, Institute for Strategic Studies, Moscow, Russia, pp. 129–134.
9. Kargina L.A. (2010), “Implementation of ELECTRE methods for assessing the competitiveness of computer firms”, Vestnik Akademii, no. 1, pp. 55–61.
10. Kats A.M. (2006), “On assessing the competitiveness of technology”, Ekonomist, no. 3, pp. 58–63.
11. Koroteev M.V. (2018), “Review of some modern trends in machine learning technology”, E-Management, no. 1, pp. 26–35, https://doi.org/10.26425/2658-3445-2018-1-26-35
12. Orlov A.I. (2004), Non-numerical statistics, MZ-Press, Moscow, Russia (in Russian).
13. Orlov A.I. (1979), “Statistics of objects of non-numerical nature and expert assessments”, In: Expert assessments. Issues of cybernetics, issue 58, Scientific Council of the Academy of Sciences of the USSR on the cybernetics complex problem, Moscow, USSR, pp. 17–33.
14. Roy B. (1968), “Classification and choice in the presence of multiple points of view (the ELECTRICAL method)” [Classement et choix en presence de points de vue multiples (la methode ELECTRE)], French journal of computer science and operational research [Revue française d’informatique et de recherche opérationnelle], vol. 2, no. 8, pp. 57–75 (in French), https://doi.org/10.1051/RO%2F196802V100571
15. Tereliansky P.V. (2009a), Non-parametric examination of objects of complex structure: monograph, Dashkov and Co., Moscow, Russia (in Russian).
16. Tereliansky P.V. (2009b), Decision support systems. Design experience: monograph, Volgograd State technical university Publ. House, Volgograd, Russia (in Russian).
Review
For citations:
Tereliansky P.V., Kuznetsov S.Yu. ELECTRE methodology and automation of alternative ranking. E-Management. 2022;5(3):26-37. (In Russ.) https://doi.org/10.26425/2658-3445-2022-5-3-26-37