Abstract
The chapter addresses the features of decision-making in multi-level hierarchical systems. The features of such systems are the set of optimum points for various individual system's components and the subsequent difficulty in coordinating and choosing optimal or quasi-optimal solutions related to conflicts of interest at different levels of management. The study describes the game-theoretic formalization of management, including control actions performed by agents, feedback flows, agents’ objective functions, i.a. utility functions. The presented formalization is based on the concept of transition to the management of multi-objective hierarchical systems via multi-agent simulation models. Based on the provided formalization, the chapter describes the organizational system of an oil production enterprise, which is a typical three-level enterprise divided into decision-making levels: strategic, tactical, and operational. By analyzing the system, we developed a conceptual management model at an oil production enterprise using a multi-agent approach. Within the developed system, agents are represented as decision-making organizations: parent organization, subsidiary, administrative and managerial personnel of the field. Thus, the result of the work is a conceptual model of a three-level multi-objective system. The prospects for the research development are: detailed elaboration of the structure of the system and the decision-making tuple; development of algorithms for the multi-agent system intelligent module, which will optimize the games in the system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Reshitko MA, Ougolnitsky GA, Usov AB (2023) Numerical method for finding nash and shtakelberg equilibria in river water quality control models. Comput Res Model 12(3): 653–667. (June 2023). https://doi.org/10.20537/2076-7633-2020-12-3-653-667
Mishina NS (2018) Problems of decision-making in hierarchical systems. In: Proceedings of the XLVII scientific and educational conference of ITMO university, Federal State Autonomous Educational Institution of Higher Education “ITMO National Research University”, St. Petersburg, pp 90–93
Mesarović MD, Macko D, Takahara Y (1970) Theory of hierarchical, multilevel, systems. mathematics in science and engineering : a series of monographs and textbooks. Academic Press
Novikov DA (1999) Mechanisms of functioning of multilevel organizational systems. Fond “Problemy upravleniya”, Moscow, Russia, pp 161
Tarasov VB (1998) Agents multi-agent systems, virtual communities: strategic direction in computer science and artificial intelligence. In: Artificial intelligence news, pp 5–63
Wernz C, Deshmukh A (2007) Decision strategies and design of agent interactions in hierarchical manufacturing systems. J Manuf Syst 26(2):135–143. https://doi.org/10.1016/j.jmsy.2007.10.003
Wernz C, Deshmukh A (2007) Managing hierarchies in a flat world. In: Proceedings of the 2007 industrial engineering research conference, Nashville, TN, pp 1266–1271
Wernz C, Deshmukh A (2009) An incentive-based, multi-period decision model for hierarchical systems. In: Proceedings of the 3rd annual conference of the Indian Subcontinent Decision Sciences Institute Region (ISDSI), Hyderabad, India, pp 12–17
Wernz C, Deshmukh A (2010) Multi-time-scale decision making for strategic agent interactions. In: Proceedings of the 2010 industrial engineering research conference, Cancun, Mexico, pp 1–6
Wernz C, Deshmukh A (2010) Multiscale decision-making: bridging organizational scales in systems with distributed decision-makers. Eur J Oper Res 202(3):828–840. https://doi.org/10.1016/j.ejor.2009.06.022
Barambones J, Imbert R, Moral C (2021) Applicability of multi-agent systems and constrained reasoning for sensor-based distributed scenarios: a systematic mapping study on dynamic DCOPs. Sensors 21(11):3807. https://doi.org/10.3390/s21113807
Vistbakka I, Troubitsyna E (2021) Modelling resilient collaborative multi-agent systems. Computing 103:535–557. https://doi.org/10.1007/s00607-020-00861-2
Nair AS, Hossen T, Campion M et al (2018) Multi-agent systems for resource allocation and scheduling in a smart grid. Technol Econ Smart Grids Sustain Energy 3:15. https://doi.org/10.1007/s40866-018-0052-y
Samigulina G, Samigulina Z (2020) Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems. Theor Biol Med Model 17:12. https://doi.org/10.1186/s12976-020-00130-x
Canese L, Cardarilli GC, Di Nunzio L et al (2021) Multi-agent reinforcement learning: a review of challenges and applications. Appl Sci 1:11
Novikov DA (2004) Institutional management of organizational systems. IPU RAN, Moscow, Russia, pp 68
Gubko MV, Novikov DA (2005) Game theory in the management of organizational systems. IPU RAN, Moscow, Russia
Ponnambalam SG, Janardhanan MN, Rishwaraj G (2021) Trust-based decision-making framework for multiagent system. Soft Comput 25(11):7559–7575. https://doi.org/10.1007/s00500-021-05715-3
Pan J (2022) Structural optimization of architectural environmental art design based on multiagent simulation system. Math Probl Eng 1–9. https://doi.org/10.1155/2022/4341816
Riekki J, Mämmelä A (2021) Research and education towards smart and sustainable world. IEEE Access 9:53156–53177. https://doi.org/10.1109/ACCESS.2021.3069902
Bolsunovskaya MV, Gintciak AM, Burlutskaya ZV, Petryaeva AA, Zubkova DA, Uspenskiy MB, Seledtsova IA (2022) The opportunities of using a hybrid approach for modeling socio-economic and sociotechnical systems. In: Proceedings of Voronezh State University. Series: systems analysis and information technologies, vol 3, pp 73–86. https://doi.org/10.17308/sait/1995-5499/2022/3/73-86
Anumbe N, Saidy C, Harik R (2022) A primer on the factories of the future. Sensors 22(15):5834. https://doi.org/10.3390/s22155834
Duan L, Da Xu L (2021) Data analytics in Industry 4.0: a survey. Inf Syst Front. https://doi.org/10.1007/s10796-021-10190-0
López-Ballesteros A, Trolle D, Srinivasan R, Senent-Aparicio J (2023) Assessing the effectiveness of potential best management practices for science-informed decision support at the watershed scale: The case of the Mar Menor coastal Lagoon, Spain. Sci Total Environ 859:160144. https://doi.org/10.1016/j.scitotenv.2022.160144
Bousdekis A, Mentzas G (2021) Enterprise integration and interoperability for big data-driven processes in the frame of Industry 4.0. Front Big Data 4:644651. https://doi.org/10.3389/fdata.2021.644651
Meenakshi N, Kumaresan A, Nishanth R, Kishore Kumar R, Jone A (2023) Stock market predictor using prescriptive analytics. Mater Today Proc 80:2159–2166. https://doi.org/10.1016/j.matpr.2021.06.153
Menezes BC, Kelly JD, Leal AG, Le Roux GC (2019) Predictive, prescriptive and detective analytics for smart manufacturing in the information age. IFAC-PapersOnLine 52(1):568–573. https://doi.org/10.1016/j.ifacol.2019.06.123
Polhill JG, Edmonds B (2023) Cognition and hypocognition: discursive and simulation-supported decision-making within complex systems. Futures 148:103121. https://doi.org/10.1016/j.futures.2023.103121
Kikuchi T, Kunigami M, Terano T (2023) Agent modeling, gaming simulation, and their formal description. In: Kaihara T, Kita H, Takahashi S, Funabashi M (eds) Innovative systems approach for facilitating smarter world. Design science and innovation. Springer, Singapore. https://doi.org/10.1007/978-981-19-7776-3_9
Grosz BJ, Kraus S, Talman S, Stossel B, Havlin M (2004) The influence of social dependencies on decision-making: initial investigations with a new game. In: Proceedings of the third international joint conference on autonomous agents and multiagent systems, 2004. AAMAS 2004, New York, NY, USA, 2004, pp 782–789
Burger K, White L, Yearworth M (2019) Developing a smart operational research with hybrid practice theories. Eur J Oper Res 277(3):1137–1150. https://doi.org/10.1016/j.ejor.2019.03.027
Lattila L, Hilletofth P, Lin B (2010) Hybrid simulation models-When, Why, How? Expert Syst Appl 37:7969–7975
Gintciak A, Burlutskaya Z, Fedyaevskaya D, Budkin A (2023) Use and processing of digital data in the era of Industry 4.0. In: Ilin I, Petrova MM, Kudryavtseva T (eds) Digital transformation on manufacturing, infrastructure & service. DTMIS 2022. Lecture notes in networks and systems, vol 684. Springer, Cham. https://doi.org/10.1007/978-3-031-32719-3_36
Acknowledgements
The research is funded by the Ministry of Science and Higher Education of the Russian Federation (contract No. 075-03-2023-004 dated 13.01.2023).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Fedyaevskaya, D.E., Burlutskaya, Z.V., Gintciak, A.M., Dixit, S. (2023). Hierarchical Cybernetic Model of Oil Production Enterprise with Distributed Decision-Making Centers. In: Bencsik, A., Kulachinskaya, A. (eds) Digital Transformation: What is the Company of Today?. Lecture Notes in Networks and Systems, vol 805. Springer, Cham. https://doi.org/10.1007/978-3-031-46594-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-46594-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46593-2
Online ISBN: 978-3-031-46594-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)