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

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.