Optimizing interaction of the components in a human-machine system of digitalization

The article considers the application of the optimization approach to ensuring efficient interaction of the components in a human-machine system of digitalization. The structure of such system is seen as a junction of ergatic elements with reference to nonergatic elements when performing a set of tasks of digital transformation. The formalized description of the two primary objectives is given, they include optimizing the number of ergatic elements involved in the support of the performance of digitalization system nonergatic elements and distributing the tasks among ergatic elements. The optimization models on a set of Boolean variables have been developed and the modifications to the algorithms of guided random search have been proposed. As the optimization criterion, a number of indicators are considered – performance, reliability and cost. The optimization model of the first objective is bicriterial. To define it, the integer values of the ergatic components that interact with the nonergatic elements in binary notation are recorded on a set of Boolean variables. The optimization problem is solved by means of guided random search managed for each iteration by changing the value of additive convolution of standard criteria values based on expert assessment of their priority ranking. Probabilistic characteristics of engagement in the search for Boolean variables are adjusted in concurrence with the development of convolution. The final solution is selected by group expert assessment. The optimization model of the second objective is created on a set of Boolean variables characterizing the engagement of the ergatic component in solving the specific problem of digitalization. At the same time, average operation time for the set of tasks in its entirety is regarded as an extreme requirement while boundary requirements are determined by the completion of each task within a single time frame using one ergatic element. Implementation of the guided random search algorithm is characterized by the awareness for multi-index representation of Boolean value variables and the means for excluding the solutions that do not comply with boundary constraints.

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