DECISION-MAKING DURING LIMITED NUMBER OF EXPERIMENTS WITH MULTIPLE CRITERIA
DOI:
https://doi.org/10.15588/1607-3274-2020-1-20Keywords:
Decision-making, multiple criteria, function of choosing, generalized mathematical programming.Abstract
Context. The mechanism of decision-making during limited number of experiments with multiple criteria are considered. The investigation object is process decision-making for project or control in complex systems with multiple criteria.
Objective. It is necessary to determine optimal (most preferred) parameters of the systems with multiple criteria. It is no the mathematical model of the system, there is limited number of experiments only.
Method. A scheme is proposed for constructing a selection mechanism for decision-making in systems with several criteria for which there is a sample of experimental results. The scheme includes the following procedures: an experimental study of a process with several criteria (functions) depending on its parameters; the use of expert evaluation to build a matrix of preferences for individual implementations; building a function of choosing preferred solutions based on a preference matrix by constructing a mathematical model of preference recognition, formulation and solving the problem of generalized mathematical programming as the final step in building the selection mechanism. The decision-making mechanism depends on the expert assessment procedure when comparing a limited set of results with each other, as well as on the statement of conditions when solving the problem of generalized mathematical programming. Comparison of a finite number of experiments is convenient for expert evaluation. Presentation of the final choice as a result of solving the problem of generalized mathematical programming is convenient for using such a mechanism in automatic control systems already without human intervention.
Results. The proposed scheme of decision-making during limited number of experiments has been applied to decision-making of project management for pellet burner. Experimental decision-making results are presented in the presence of several criteria for a pellet burner of a tubular heater, which confirm the acceptability of the developed decision-making mechanism.
Conclusions. It was proposed the new scheme for constructing a selection mechanism for decision-making in systems with several criteria where there is a sample of experimental results only. The scheme of decision-making is includes the solving the problem of generalized mathematical programming as the final step in building the selection mechanism. For the solving the problem of generalized mathematical programming may be applied the evolution search algorithm.
References
Fishburn P. Representiable choice function, Econometrica, 1976, Vol. 44, No. 5, pp. 1033–1043.
Ajzerman M. A., Aleskerov F. T. Vibor variantov: osnovi teorii. Мoscow, Nauka, 1990, 240 p. ISBN 5-02-014091-0.
Ajzerman M.A. Nekotorie novie zadachi obchej teorii vibora (obzor odnogo napravleniya issledovaniy), Avtomat. i telemeh., 1984, No. 9, pp. 5–43.
Sholomov L.A. Logicheskie metodi postroeniya i analiza modeley vibora. Мoscow, Nauka, 1989, 288 p. ISBN 5-02014108-9.
Ivakhnenko A.G. Heuristic Self-Organization in Problems of Engineering Cybernetics, Automatica, 1970, Vol. 6, pp. 207–219.
Ivakhnenko A.G. Polynomial Theory of Complex Systems, IEEE Transactions on Systems Man and Cybernetics, 1971, Vol. 4, pp. 364–378.
Lemarchand L., Masse D., Rebreyend P., Hakansson J. Multiobjective optimization for multimode transportation problems, Advances in Operations Research, 2018, Vol. 2018, 13 p. https:// doi.org/10.1155/2018/8720643.
Sagawa M., Kusuno N., Aguirre H., Tanaka K., Koishi M. Evolutionary multiobjective optimization including practically desirable solutions, Advances in Operations Research, Article ID 9094514, 2017, Vol. 2017, 16 p. https:// doi.org/10.1155/2017/9094514.
Giagkiozis I., Fleming P. J. Pareto front estimation for decision making, Evolutionary computation, 2014, Vol. 22, No. 4, pp. 651–678. htts: //www.researchgate/publication/261369702.
Wang Y., Sun X. A many-objective optimization algorithm based on weight vector adjustment, Computational Intelligence and Neuroscience, 2018, Vol. 2018, Article ID 4527968, 21 p. DOI: 10.1155/2018/4527968.
Yudin D. B. Generalized mathematical programming, Economics and Mathematical Methods, 1984, Vol. 20, pp. 148– 167.
Judin D.B. Vichislitelnie metodi teorii prinyatiya resheniy / D.B. Judin. – М. : Nauka, 1989. – 320 p.
Kolbin V. V. Generalized mathematical programming as a decision model, Applied Mathematical Sciencies, 2014, Vol. 8, No. 70, pp. 3469–3476. DOI 10.12988/ams.2014.44231.
Irodov V. F., Maksimenkov V. P. Application of an evolutionary program for solving the travelling-salesman problem, Sov. Autom. Control ; translation from Avtomatika, 1981, No. 4, pp. 7–10. io-port.net Database 03803175.
Irodov V. F. The construction and convergence of evolutional algorithms of random search for self-organization, Sov. J. Autom. Inf. Sci. 20 ; translation from Avtomatika, 1987, No.4, pp. 34–43. io-port.net Database 04072731.
Irodov V. F. Self-organization methods for analysis of nonlinear systems with binary choice relations, System Analysis Modeling Simulation, 1995, Vol. 18–19, pp. 203– 206. https://dl.acm.org/citation.cfm?id=208028#.
Irodov V. F., Khatskevych Yu. V. Convergence of evolutionary algorithms for optimal solution with binary choice relations, Stroitelstvo, materialovedenie, mashinostroenie : Sb. nauch. trudov. Dnepr, 2017, Vol. 98, pp. 91–96. http://nbuv.gov.ua/UJRN/smmeect_2017_98_16.
Barsuk R. V., Irodov V. F. Matematichne modeliuvannya funkcii viboru perevazhnih rishen dlya trubchastih gazovih nagrivachiv za eksperimentalnoiy informazieiy, Visnik Pridneprovskoi derzhavnoi akademii budivniztva ta architecturi : zb. nauk. prats. Dnipro, 2016, No. 8(221), pp. 17–25. http://oaji.net/articles/2017/2528-1507122475.
Subbotin S. A. The neuro-fuzzy network synthesis and simplification on precedents in problems of diagnosis and pattern recognition, Optical Memory and Neural Networks (Information Optics), 2013, Vol. 22, No. 2, pp. 97–103. DOI: 10.3103/s1060992x13020082.
Subbotin S. A. Methods of sampling based on exhaustive and evolutionary search, Automatic Control and Computer Sciences, 2013, Vol. 47, No. 3, pp. 113–121. DOI: 10.3103/s0146411613030073.
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