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Practice Analysis of Effectiveness Components for the System Functioning Process: Energy Aspect

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Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2021)

Abstract

The article presents the author’s measuring method for the effectiveness of the functioning processes the system’s components based on the models of the efficiency components in terms of the energy approach and reports the applied aspects of its application. The three types of the rates for measurement effectiveness of the processes functioning system’ components outlined in previously published author’s works, were analyzed. The newest approaches to a method of measurement of newly created authoritative rates for effectiveness of components of process of functioning of system are presented. These approaches are based on the use of the share of profits and cost of processes in total production, and are implemented on the examples of specific industrial enterprises. The technique of approaches differs slightly from the technique used in the examples presented in the previous author’s work, making it more accurate. In this sense, innovation lies in the fact that our approaches solve the problem of simultaneous measurement of the efficiency of the system components (using the effectiveness rates of the three indicated types) regardless of the units of measurement of their total, net production and costs, since all this boils down to a dimensionless unit of measurement of the share of benefits and of the share of costs in their total production, moreover, the average values of the effectiveness rates of the functioning of the components of the system to some extent (approximately) can be considered as characteristics of the corresponding rates for the process of the functioning of the system. The examples show the practical implementation of this technology.

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Yarmolenko, V. et al. (2022). Practice Analysis of Effectiveness Components for the System Functioning Process: Energy Aspect. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_19

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