Economic-mathematical model of optimization of expenses investment-building project payment automobile roads

Authors

  • O. М. Zagursky National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • V. V. Kukanov National University of Life and Environmental Sciences of Ukraine image/svg+xml

Abstract

Annotation. The multivariance of the methods of developing investment and construction projects leads to the need to analyze existing and construct new economic and mathematical models that take into account the likelihood of the implementation of certain options, and also take into account the conditions of uncertainty and risk. In the field of motor transport infrastructure, investment and construction projects are always large-scale and costly, because their implementation affects different components of socio-economic development of territories. Investment-building projects of toll roads, in general, are difficulty managed systems that change their configuration in the course of multi-step processes. The ability to manage such a system causes the problem of choosing one or another action and inevitably leads to the task of finding the optimal solution in terms of management.
In the article an algorithm of the economic-mathematical model of optimization of expenses of investment-construction project of a paid motorway in the conditions of uncertainty and risk is developed. The description of the system's work is based on the "Markovian" decision-making processes, and optimization of expenses was carried out by methods of discrete and dynamic programming, in accordance with the general concept of analysis and optimization of multi-step tasks.
The proposed model can be a tool in the operational management of the process of developing an investment and construction project, on the basis of which will be further developed the schedules and calendar plan for its implementation.
Key words: costs, dynamic programming, investment-construction project, economic-mathematical model, paid motorway, risk.

 

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2019-01-25

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