Managing industrial complexes: A hierarchical agent-oriented model
Among the system problems in managing an industrial complex are the difficulties in determining its composition, coordinating the goals of management subjects, and formalizing the conditions for optimal management. The paper examines the processes of forecasting the state of an industrial complex an...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Ural State University of Economics
2023-01-01
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| Series: | Управленец |
| Subjects: | |
| Online Access: | https://upravlenets.usue.ru/en/issues-2022/1143 |
| Summary: | Among the system problems in managing an industrial complex are the difficulties in determining its composition, coordinating the goals of management subjects, and formalizing the conditions for optimal management. The paper examines the processes of forecasting the state of an industrial complex and managing its potential most effectively at different hierarchical levels. The theoretical framework of the study is the network approach to the subject-object structure of the industrial complex viewed as a complex dynamic system. The method of agent-oriented modelling was used as having certain advantages in comparison with neoclassical equilibrium models. For the processes in question, we propose utilizing a deterministic economicmathematical model, in which the dynamics of the main factors (phase vectors) is described by the relevant vector linear discrete recurrent equations in the presence of control actions. The developed management system has three levels of decision-making: (1) the dominating (federal) level controlled by an aggregated federal agent; (2) the first subordinate (regional) level controlled by aggregated regional agents; and (3) the second subordinate (enterprises) level controlled by production agents. The article presents a general scheme for solving the problems of forecasting the state of and optimizing the management of the production potential using the developed three-level hierarchical discrete controlled dynamic system. The results obtained can be used in the design of intelligent computer systems for information supply and managerial decision-making. |
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| ISSN: | 2218-5003 2686-7923 |