Formation of an Adaptive Decision-Making Support Means Components in Engineering Infrastructure Reconstruction Programs Management

Authors

  • Illia Khudiakov O.M. Beketov National University of Urban Economy in Kharkiv

DOI:

https://doi.org/10.33042/2079-424X.2023.62.1.02

Keywords:

decision support, adaptive management, program management, engineering infrastructure

Abstract

The article is devoted to a decision-making support tool aimed at improving the efficiency of engineering infrastructure reconstruction program management in the context of developing the architecture and hierarchical structure of program work and program architecture management. As part of the study, the main components of the model are defined, which include a set of decision-maker preferences, decision-making tasks, sets of input data, and applied software components of the model. To support decision-making, the adaptive model applies the method of system modeling and forecasting the value of the objective function at a given system configuration. Forecasting is done using machine learning methods based on a dataset consisting of historical data related to existing engineering systems. The work describes the components of the redistribution of varied model parameters, which modify the model dataset based on the selected object type, which allows adapting the decision-making process to the existing program implementation goals. A description of the data post-processing process is provided, which allows the decision-maker to obtain information about the influence of the main parameters of the system on the target indicator. The main differences between the described adaptive decision support model and the currently existing tools have been determined. The application of the developed adaptive model is possible in the management of programs for the reconstruction of such engineering systems as systems of heat, gas, electricity supply, water supply and drainage, etc.

Author Biography

Illia Khudiakov, O.M. Beketov National University of Urban Economy in Kharkiv

Postgraduate student

References

Argent, R.M. (2009). Components of adaptive management. In: Allan, C., Stankey, G.H. (eds.) Adaptive Environmental Management (pp. 11–36). Springer. https://doi.org/10.1007/978-1-4020-9632-7_2

Blokdyk, G. (2018). Adaptive Program Management a Clear and Concise Reference. 5StarCooks

Silber, A. (2017). Adaptive Project Management: Leading Complex and Uncertain Projects. BoockLocker

Wysocki, R.K., & McGary, R. (2006). Effective Project Management. Wiley

Feldbaum, A. (1960). Dual management theory I. Automatics and Telemechanics, XXI(9), 1240–1249.

Feldbaum, A. (1960). Dual management theory II. Automatics and Telemechanics, XXI(11), 1453-1464.

Feldbaum, A. (1961). Dual management theory III. Automatics and Telemechanics, XXII(1), 3–16.

Sukhonos, M. (2016). Dual Energy Infrastructure Portfolio Management in a Dynamic Environment. O.M. Beketov National University of Urban Economy in Kharkiv [in Ukrainian]

Bolling, R.J., & Van der Wijk, K. (2019). Practice note. Adaptive programme management in fragile and complex settings. The Broker. https://www.thebrokeronli ne.eu/practice-note-adaptive-programme-management-infragile-and-complex-settings/

Keen, P.G. (1980). Adaptive design for decision support systems. ACM SIGOA Newsletter, 1(4-5), 15–25. https://doi.org/10.1145/1017654.1017659

Chuang, T.T., & Yadav, S.B. (1998). The development of an adaptive decision support system. Decision Support Systems, 24(2), 73–87. https://doi.org/10.1016/S0167-9236(98)00065-7

Fazlollahi, B., Parikh, M.A., & Verma, S. (1997). Adaptive decision support systems. Decision Support Systems, 20(4), 297–315. https://doi.org/10.1016/S0167-9236(97)00014-6

Azevedo, G.B.D., Maccari, E.A., & Asgary, N.(2021). The use of adaptive project management practices and methodologies in the development of a professional doctoral program. Revista de Administração da UFSM, 14, 44–62. https://doi.org/10.5902/1983465942849

Samrah, R.A., Shaalan, K., & Ali, A.A. (2017). System dynamics modeling for the complexity of knowledge creation within adaptive large programs management. In: Rocha, Á., et al. (eds.) Recent Advances in Information Systems and Technologies. AISC, vol. 569 (pp. 3–17). Springer. https://doi.org/10.1007/978-3-319-56535-4_1

Ciapessoni, E., Cirio, D., Pitto, A., & Sforna, M. (2020). A quantitative methodology to assess the process of service and infrastructure recovery in power systems. Electric Power Systems Research, 189, 106735. https://doi.org/10.1016/j.epsr.2020.106735

Shesho, I.K., Filkoski, R.V., & Tashevski, D.J. (2018). Techno-economic and environmental optimization of heat supply systems in urban areas. Thermal Science, 22(5), 1635–1647. https://doi.org/10.2298/TSCI18S5635SLighting Engineering & Power Engineering (ISSN 2079-424Х / eISSN 2415-3923) 16

Ancona, M.A., Bianchi, M., Branchini, L., & Melino, F. (2014). District heating network design and analysis. Energy Procedia, 45, 1225–1234. https://doi.org/10.1016/j.egypro.2014.01.128

Mehleri, E.D., Sarimveis, H., Markatos, N.C., &Papageorgiou, L.G. (2012). A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level. Energy, 44(1), 96–104. https://doi.org/10.1016/j.energy.2012.02.009

Sameti, M., & Haghighat, F. (2019). Optimization of 4th generation distributed district heating system: Design and planning of combined heat and power. Renewable Energy, 130, 371–387. https://doi.org/10.1016/j.renene.2018.06.068

Zheng, X., Wu, G., Qiu, Y., Zhan, X., Shah, N., Li, N., & Zhao, Y. (2018). A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China. Applied Energy, 210, 1126–1140. https://doi.org/10.1016/j.apenergy.2017.06.038

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Published

2023-04-29

How to Cite

Khudiakov, I. (2023). Formation of an Adaptive Decision-Making Support Means Components in Engineering Infrastructure Reconstruction Programs Management. Lighting Engineering & Power Engineering, 62(1), 12–16. https://doi.org/10.33042/2079-424X.2023.62.1.02

Issue

Vol. 62 No. 1 (2023): Lighting Engineering & Power Engineering

Section

Power Engineering

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