AN ADAPTIVE DECISION-MAKING SUPPORT MODEL IN THE MANAGEMETN OF ENGINEERING INFRASTRUCTURE RECONSTRUCTION PROGRAMS AND PROJECTS
DOI:
https://doi.org/10.33042/2522-1809-2023-4-178-2-9Keywords:
adaptive program management, adaptive project management, decision support systems, machine learningAbstract
Reconstruction of engineering infrastructure has become an important topic for Ukraine since the beginning of the full-scale invasion of russian federation in 2022. Standard approach to implementation of programs and projects is inefficient for turbulent environments and therefore the use adaptive approach is relevant.
The concept of adaptive management has been analyzed. Means and instruments of adaptive management were analyzed. It was defined that different means and instruments of adaptive management are relevant for different program implementation phases. For the delivery phase these are learning and forecasting, for the closure phase – analysis of obtained experience for more efficient implementation of the next programs, for the definition phase – instruments that can ensure the further implementation of adaptivity to the management processes.
An adaptive decision-making support model concept is proposed for adaptive engineering infrastructure reconstruction programs and projects management. The model is based on machine learning methods and can be used for program architecture and project work structure development and management. In this case the decision-making consists in choosing the optimal composition and configuration of the system that is the reconstruction object from among the available alternatives by predicting the values of the parameters of its elements in order to minimize the costs of implementing the program or the project.
The model is created with Microsoft Azure Machine Learning Studio, the user interface is created in Microsoft Excel.
The distinguishing features of the model are adaptivity due to the use of machine learning methods, possibility of scaling of the model to ensure its application to different system levels and presence of post-processing instruments for different use cases including calculation of additional parameter values, parameter values dependency graphs construction etc.
The dataset for the model consists of several parameter categories that characterize the system modelled: technical and technological parameters, environmental parameters, energy efficiency parameters, energy security parameters, economic parameters, operational safety parameters.
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