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Fuzzy and interval-probabilistic methods of risk assessment of the investment project implemented by public private partnership
Ostrouško, Viktorie ; Synek, Miloslav (advisor) ; Ivánek, Jiří (referee) ; Scholleová, Hana (referee)
The result of my dissertation justifies the use of fuzzy-sets theory to make a prediction of cost risk of a PPP project, when there is not enough information available to clearly describe the project, and, when the probability distributions of the variables that characterize the project are unknown. I showed that fuzzy-sets theory and linguistic variables may be effectively used in such a case. In this thesis were classified different types of uncertainty and investigated traditional methods for estimating efficiency of a investment project in conditions of uncertainty. On the basis of the analysis were offered new ways of conducting risk analysis for PPP projects with use of fuzzy sets theory. The main goal was to create an application model for risk assessment of the PPP project which, with a high degree of reliability, suggests a general assessment of situation. The goal set in my work was met. Model of risk assessment of the project proposed by me gives more stable results in comparison with the probabilistic model. For comparison were used different types of probability distribution functions and membership functions. The following conclusions and statements describe the novelty of the work on fuzzy logic and economic theory: develops a method of cash-flow (future expenditure connected with the appearance of risk) modeling of investment project in fuzzy environment, demonstrates the use of fuzzy sets theory in projects analyses and describes how to calculate and interpret this value, demonstrates example of the use of results applied to the analysis of infrastructure development project in Moscow, Russia. The possibility of using this method is not only in the analysis of infrastructure development projects, but also in realization of non-commercial projects by social institutes and government agencies.
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Coupling the Worst Scenario Method with Fuzzy Input Data
Chleboun, Jan
Inputs entering a mathematical model are considered uncertain, i.e., they are not given in a crisp form but only within a set of admissible data. The solution to the model is evaluated through a functional (criterion) reflecting a particular feature of the solution, e.g., local temperature, velocity, stress. The higher value, the "worse" solution. The goal of the worst scenario method...
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