Analytical method for quantification of economic risks during feasibility analysis for large engineering projects

The objectives of this thesis are to develop an analytical method for economic risk quantification during feasibility analysis for large engineering projects and to computerize the method to explore its behavior, to validate it and to test its practicality for the measurement of uncertainty of decision variables such as project duration, cost, revenue, net present value and internal rate of return.

The method is developed by applying a risk measurement framework to the project economic structure. The risk measurement framework is developed for any function Y = g(X), between a derived variable and its correlated primary variables. Using moment analysis and the Pearson family of distributions, the uncertainty of the derived variable is quantified as a cumulative distribution function. The moments for the primary variables are evaluated from subjective estimates elicited from experts. The correlations between the primary variables are elicited as positive definite correlation matrices. The project economic structure describes an engineering project in three hierarchical levels, namely, work package/revenue stream, project performance and project decision. Each of these levels can be described by Y = g(X), with the derived variables of the lower levels as the primary variables for the upper level.

Project duration is estimated by combining the generalized PNET algorithm to the project economic structure. This permits the evaluation of the multiple paths in the project network. Also, the limiting values of the PNET transitional correlation (0,1) permits the estimation of bounds on all of the derived variables. Project cost and revenue are evaluated in terms of current, total and discounted dollars, thereby emphasizing the economic effects of time, inflation and interest on net present value and internal rate of return. The internal rate of return is evaluated from a variation of Hillier's method.

The analytical method is validated using Monte Carlo simulation. The validations show that the analytical method is a comprehensive and extremely economical alternative to Monte Carlo simulation for economic risk quantification of large engineering projects.