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Managing industrial complexes: A hierarchical agent-oriented model

Author

Listed:
  • Viktoriya V. Akberdina

    (Institute of Economics of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia)

  • Andrey F. Shorikov

    (Institute of Economics of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia)

Abstract

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.

Suggested Citation

  • Viktoriya V. Akberdina & Andrey F. Shorikov, 2022. "Managing industrial complexes: A hierarchical agent-oriented model," Upravlenets, Ural State University of Economics, vol. 13(6), pages 2-14, January.
    • Handle: RePEc:url:upravl:v:13:y:2022:i:6:p:2-14
    DOI: 10.29141/2218-5003-2022-13-6-1
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    References listed on IDEAS

    as
    1. G. B. Korovin, 2020. "Architecture of the agent-based model for the region’s industrial complex digital transformation," Journal of New Economy, Ural State University of Economics, vol. 21(3), pages 158-174, October.
    2. Paul Windrum & Giorgio Fagiolo & Alessio Moneta, 2007. "Empirical Validation of Agent-Based Models: Alternatives and Prospects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-8.
    3. Luigi Orsenigo & Fabio Pammolli & Massimo Riccaboni & Andrea Bonaccorsi & Giuseppe Turchetti, 1997. "The Evolution of Knowledge and the Dynamics of an Industry Network," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 1(2), pages 147-175, June.
    4. Gordon Walker & Bruce Kogut & Weijian Shan, 1997. "Social Capital, Structural Holes and the Formation of an Industry Network," Organization Science, INFORMS, vol. 8(2), pages 109-125, April.
    5. Amir, Rabah & Lazzati, Natalia, 2011. "Network effects, market structure and industry performance," Journal of Economic Theory, Elsevier, vol. 146(6), pages 2389-2419.
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    Cited by:

    1. Andrey F. Shorikov & Grigory B. Korovin & Dmitry V. Sirotin, 2023. "Methodology for regional industrial complex management: Architecture of an agent-based model," Upravlenets, Ural State University of Economics, vol. 14(6), pages 63-76, December.

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    Keywords

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    JEL classification:

    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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