This study investigates the optimization of management in a digitalized organizational system (DOS) through the lens of information interaction among participants in the ordering process. The primary focus is on the development of a structural model of the system and a management decision-making algorithm. The structural model of the organizational system is defined by the connections facilitated through an information interaction environment between the management center and the entities of producers, consumers, and aggregators. These connections are characterized by quantitative values of the DOS performance and resource indicators. One subset of these indicators is established as requirements, while the other is recorded during monitoring at specific time periods. The structural model of the management decision-making process is, firstly, defined by the combination of an expert mode and an intelligent support mode, and, secondly, by two stages: operation and development, within a given planning horizon. For these modes and stages, resource allocation management modules are substantiated: for allocating resources among consumer information flows during order formation; for allocating ergodic elements among non-ergodic elements of the DOS during order fulfillment; and for development conditions of the DOS within a given planning horizon. Intelligent support for management decisions is implemented through the integration of optimization, predictive modeling, and expert assessment. The foundation for managing the allocation of resources among information flows is a parametric optimization model, while the allocation of ergodic elements is based on a multi-alternative optimization model. For making decisions on the allocation of DOS development resources, the application of predictive models, trained via machine learning on retrospective monitoring data, is considered.
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Pupykin Alexey Nikolaevich
Voronezh Institute of High Technologies
Voronezh, Russian Federation
