FORMATION AND APPLICATION OF AN AGENT-ORIENTED MODEL IN THE MANAGEMENT OF THE OIL INDUSTRY OF THE REPUBLIC OF KAZAKHSTAN

G. Azieva, A. Alimagambetova, U. Turusbekova

Аннотация


Kazakhstan is one of the few countries in the world rich in oil, deservedly called “black gold” because it is the most important source of energy. The relevance of the study of this paper is determined by the fact that the management of the oil industry affects not only the management process itself, but also the social aspects of the implementation of the development strategy of the state as a whole. It is necessary to identify aspects of management activity and define criteria by which it is possible to calculate the effectiveness of managerial decision-making in the analyzed industry. Agent models allow us to identify the main criteria for the effectiveness of managerial decision-making and optimize social and economic costs for their implementation within the framework of interdepartmental planning. The novelty of the research is determined by the fact that agent models are based not only on the associated parameters of the management process, but also affect the possibility of planning current activities for a long period. The article shows that the formation of agent models should affect both the aspect of the formation of matrices of complex managerial actions and calculations on the accounting of competencies in making managerial decisions. The practical significance of the study is determined by the fact that the development of complex models based on agent forms allows expanding the use of forms of control over the industry by the state and other stakeholders. The implementation of a matrix form of management is proposed, taking into account balanced industry indicators of management quality.

Ключевые слова


agent, management, model, industry, oil

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Литература


Leitner, S., & Wall, F. (2021). Decision-facilitating information in hidden-action setups: An agent-based approach. Journal of Economic Interaction and Coordination, 16(2), 323-358. https://doi.org/10.1007/s11403-020-00297-z

Parygin, D., Usov, A., Burov, S., Sadovnikova, N., Ostroukhov, P., & Pyannikova, A. (2019, November). Multi-agent approach to modeling the dynamics of urban processes (on the example of urban movements). In International Conference on Electronic Governance and Open Society: Challenges in Eurasia, (pp. 243-257). Springer, Cham. https://doi.org/10.1007/978-3-030-39296-3_18

Liu, Y., Jiang, Q., Liang, Z., Wu, Z., Liu, X., Feng, Q., ... & Guo, H. (2021). Lake eutrophication responses modeling and watershed management optimization algorithm: A review. Journal of Lake Sciences, 33(01), 49–63. https://doi.org/10.18307/2021.0103

Mirzaei, A., & Zibaei, M. (2021). Water conflict management between agriculture and wetland under climate change: Application of economic-hydrological-behavioral modelling. Water Resources Management, 35(1). https://doi.org/10.1007/s11269-020-02703-4

Zhuge, C., Bithell, M., Shao, C., Li, X., & Gao, J. (2021). An improvement in MATSim computing time for large-scale travel behaviour microsimulation. Transportation, 48(1), 193–214. https://doi.org/10.1007/s11116-019-10048-0

Dhamija, P., & Bag, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal. 32(4), 869–896. https://doi.org/10.1108/TQM-10-2019-0243

Kuklová, J., & Přibyl, O. (2019, May). Framework Model in Anylogic for Smart City Ring Road Management. In 2019 Smart City Symposium Prague (SCSP). https://doi.org/10.1109/SCSP.2019.8805681

Mewes, B., & Schumann, A. H. (2019). An agent-based extension for object-based image analysis for the delineation of irrigated agriculture from remote sensing data. International Journal of Remote Sensing, 40(12), 4623–4641. https://doi.org/10.1080/01431161.2019.1569788

Ghorbani, A., Ho, P., & Bravo, G. (2021). Institutional form versus function in a common property context: The credibility thesis tested through an agent-based model. Land Use Policy, 102, 105237. https://doi.org/10.1016/j.landusepol.2020.105237

Nouri, A., Saghafian, B., Delavar, M., & Bazargan-Lari, M. R. (2019). Agent-based modeling for evaluation of crop pattern and water management policies. Water Resources Management, 33(11), 3707–3720. https://doi.org/10.1007/s11269-019-02327-3

Patwary, A. U., Huang, W., & Lo, H. K. (2021). Metamodel-based calibration of large-scale multimodal microscopic traffic simulation. Transportation Research Part C: Emerging Technologies, 124, 102859. https://doi.org/10.1016/j.trc.2020.102859

Gomez, M., Weiss, M., & Krishnamurthy, P. (2019). Improving liquidity in secondary spectrum markets: Virtualizing spectrum for fungibility. IEEE Transactions on Cognitive Communications and Networking, 5(2), 252-266. https://doi.org/10.1109/TCCN.2019.2901787

Kravari, K., & Bassiliades, N. (2019). StoRM: A social agent-based trust model for the internet of things adopting microservice architecture. Simulation Modelling Practice and Theory, 94, 286-302. https://doi.org/10.1016/j.simpat.2019.03.008

Zinkin, S. A., Mehanov, V. B., Karamisheva, N. S., & Volchihin, V. I. (2019, August). Organization of Autonomous Agent-Robots Interactions for Managing a Very Large Distributed Database System in a Metacomputer Environment. In 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR), (pp. 846-851). IEEE. https://doi.org/10.1109/RCAR47638.2019.9044125

Xiong, L., Li, P., Wang, Z., & Wang, J. (2020). Multi-agent based multi objective renewable energy management for diversified community power consumers. Applied energy, 259, 114140. https://doi.org/10.1016/j.apenergy.2019.114140

Haque, N., Tomar, A., Nguyen, P., & Pemen, G. (2020). Dynamic tariff for day-ahead congestion management in agent-based LV distribution networks. Energies, 13(2), 318. https://doi.org/10.3390/en13020318

Heidary, M. H., & Aghaie, A. (2019). Risk averse sourcing in a stochastic supply chain: A simulation-optimization approach. Computers & Industrial Engineering, 130, 62–74. https://doi.org/10.1016/j.cie.2019.02.023

Owusu, K. A., Acevedo-Trejos, E., Fall, M. M., & Merico, A. (2020). Effects of cooperation and different characteristics of Marine Protected Areas in a simulated small-scale fishery. Ecological Complexity, 44, 100876. https://doi.org/10.1016/j.ecocom.2020.100876

Li, X., Pu, W., & Zhao, X. (2019). Agent action diagram: Toward a model for emergency management system. Simulation Modelling Practice and Theory, 94, 66-99. https://doi.org/10.1016/j.simpat.2019.02.004

Park, A. J., Patterson, L. D., Tsang, H. H., Ficocelli, R., Spicer, V., & Song, J. (2019, November). Devising and optimizing crowd control strategies using agent-based modeling and simulation. 2019 European Intelligence and Security Informatics Conference (EISIC), IEEE, 78-84. https://doi.org/10.1109/EISIC49498.2019.9108875




DOI: http://dx.doi.org/10.37943/AITU.2022.41.73.004

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Нур-Султан
Бизнес-центр EXPO, блок C.1.
Казахстан, 010000

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