Improvement of the Investment Decision–making Model in Conditions of Uncertainty

The paper considers the problem of investment project evaluation in conditions of uncertainty. The urgency of considered problem is caused by the impossibility of accurate and reliable assessment of project parameters predictive values. The literature on the subject is analyzed and the main methods used to evaluate the effectiveness of investment projects are analyzed. The proposed approach for investment projects evaluation has been described and implemented using software tools, taking into account the uncertainty factor. The developed model was adapted, applied and tested on real investment projects. As a result, the investment decision-making model has been improved and expanded to solve the problem of investment projects portfolio selection. The methodology of the study includes the practical application of the analytic hierarchy process, the probabilistic approach and Monte Carlo simulation modeling as well as heuristic evolutionary genetic algorithm with the Pareto efficiency criterion in order to solve the problem of several investment projects selection formulated in the form of multicriteria optimization problem with discrete nonlinear stochastic programming.