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JOURNALS // Vestnik Yuzhno-Ural'skogo Universiteta. Seriya Matematicheskoe Modelirovanie i Programmirovanie // Archive

Vestnik YuUrGU. Ser. Mat. Model. Progr., 2018 Volume 11, Issue 1, Pages 27–34 (Mi vyuru415)

This article is cited in 3 papers

Mathematical Modelling

Optimization of training modules choice during multipurpose training of specialists

V. V. Menshikh, E. N. Sereda

Voronezh Institute of the Ministry of the Interior of the Russian Federation, Voronezh, Russian Federation

Abstract: The article is devoted to the problems of mathematical modelling of the processes of organizing multipurpose learning. Under the multipurpose training is understood such an organization of the educational process, in which in one group specialists are trained in several related areas of activity, trajectory of training for which at certain intervals intersect. In order to reduce the total training time, as well as the cost or resources required in the training process, it is expedient to carry out the dynamic grouping of students by subgroups in order to master certain competences.
The development of the mathematical apparatus used to optimize the multipurpose learning process has not been completely studied at present. To reduce the dimension of the overall task of optimizing the process of organizing multipurpose training, its step-by-step solution is proposed. The article describes the approach to calculating estimates of the possibility of training specialists in the areas of training and selection of training modules available in the educational organization. The paper considers the options for optimizing the selection of modules for training specialists on the following criteria: minimizing the total duration of training, the cost of training and resources used for training. The algorithm with the help of which it is possible to form an optimal group of students is proposed.

Keywords: multipurpose training simulation; field of training; optimization; assignment problem.

UDC: 519.168

MSC: 93A30

Received: 25.01.2018

Language: English

DOI: 10.14529/mmp180103



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