А model and a numerical method for optimizing the choice of a training trajectory for heterogeneous groups of specialists

Training and retraining of specialists of different profiles at present requires taking into account the high dynamics of the conditions of their professional activity. This is especially relevant when it is necessary to train specialists to act in emergency situation. For this reason, two major problems with organisation of the training process of specialists have arisen:
$\bullet$ the requirement for simultaneous training of a heterogeneous group which consists of specialists of different profiles who jointly provides the solution of a certain range of tasks in case of emergencies;
$\bullet$ the requirement for minimizing the duration of the training process.
Both universal and individual competences are expected of specialists in heterogeneous groups. In particular, in heterogeneous groups that prepare for emergency response, universal competences are required to act in special circumstances and individual competences are required to fulfil narrow professional tasks.
The said circumstance makes it possible to organize the sequence of courses for training specialists in the groups under consideration in such a way that it is possible to obtain universal competences in one course simultaneously by specialists of different profiles, which allows reducing the total training time of the whole heterogeneous group. At the same time, it is necessary to take into account the capabilities of the educational organisation in terms of the number of simultaneous trainees in each course, which ensures the acquisition of the relevant competence.
In this regard, there is a need to optimize the choice of trajectory, i.e., the sequence of courses, for training specialists in heterogeneous groups, taking into account the capacity of the educational organisation that trained them. For this purpose, we have developed a mathematical model and a numerical method for finding the optimal trajectory based on the use of genetic algorithm, the advantage of which is polynomial computational complexity. A numerical example is presented.