| This dissertation focuses on the application of real options analysis to system capacity planning under uncertainty. The study recognizes uncertainty arising from two main sources: randomness (the chance that something happens), and fuzziness (biases, vagueness, and imprecision).;Established on the principles of financial options theory, real options analysis focuses on uncertainty arising from randomness. Its application is well-suited for evaluating strategic decision alternatives that are constrained by uncertainty resulting from randomness. The application of real options analysis to strategic decision-making provides a framework that allows decision-makers to systematically mitigate the negative impact of uncertainty, while at the same time offers them the ability to take advantage of opportunities created as the uncertainty is resolved.;The application of real options analysis is gaining popularity in the systems and industrial engineering domain, and has been applied to a wild range of strategic decision analysis problems, especially those found in capital-intensive industries such as infrastructure development, pharmaceutical research and development, and natural resource exploration. It can be used to enhance the system capacity planning and decision-making process because of its effectiveness in accounting for uncertainty and managerial flexibility; wherein decisions are aligned with risk preferences, and potential opportunities created through managerial flexibility and the resolution of uncertainty. A multiphase system capacity planning model, grounded on real options theory, is developed. Our proposed real options framework has the characteristics of sequential compound options. By doing so, we describe the investment decision process in system capacity planning as an N-phase project. Projects implementations are divided into multiple phases, and at each decision point progress is reviewed, and market and technical uncertainties are resolved. The main goal of this framework is to enrich the system capacity planning and decision-making process to efficiently deploy the system's resources, while ensuring that output/performance meets its intended objectives. The framework is validated using case methodology to analyze a strategic system capacity planning problem. This problem involves planning for the development of electricity generation in Liberia. Liberia is a West African nation emerging from over a decade of civil conflict, and is in the process of rebuilding a stable environment for long-term growth and development. Results from the case study support claims for the application of real option analysis to evaluating strategic decision problems (in this case, system capacity planning) with long planning horizon.;Finally, we enhanced our analysis to account for uncertainty arising from fuzziness. This is accomplished with the application of Fuzzy Set Theory. The electricity demand forecasts used for estimating input parameters to our real option model are defined as expected values of trapezoidal fuzzy variables. This approach is suitable for filling the inadequacy of our real options model to account for uncertainty arising from fuzziness. This research adds to ongoing effort in systems engineering and the capital investment sciences to find better approaches to analyze strategic decision-making problems. |
