Abstract

Implementation of investment projects contributes to the development of each metallurgical enterprise. An important project management process is project time management, which includes forecasting and monitoring compliance with deadlines, monitoring the content and relationships between works, as well as assessing the resources required for each work. When planning a project, it is often not possible to accurately determine the duration of a network schedule. Uncertainty of work time is associated with a certain degree of uniqueness of each work and external factors influencing this work. The method of network planning is recognized as one of the most effective methods used in the theory of project time management. Thanks to network models, it is possible to determine one of the key characteristics of the project, namely its duration. Due to the fact that it is not possible to accurately determine the duration of a project, so it is obvious the relevance of the use of network models that allow you to take into account the uncertainty in the time of work. The purpose of this work is to develop a probabilistic simulation model for calculating the duration of the project to create a combined gas turbine power plant at a metallurgical enterprise. It is proposed to take into account the uncertainty in the duration of the project in the traditional way, it is assumed that the duration of the project has a beta distribution. The work uses a network schedule for the creation of a combined gas turbine power plant. And all the works of the project were divided into appropriate stages. With the help of experts who provided a pessimistic, optimistic and most likely estimate of the duration of the work, the parameters of the beta distribution were determined. As part of the algorithm for calculating the simulation model of the project, 200 implementations of a random variable - the duration of work were generated and, accordingly, 200 implementations of the critical path of the project were obtained. the main characteristics of the specified set of random numbers are determined. Then we found a value for the duration of the critical path, which with a probability of 95% will not exceed 21 months.

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