An essential task of airport authorities is to plan airport facilities that meet future traffic needs in a dynamic and uncertain environment. Major technical difficulties in airport facility development stem from: (1) distinct construction and operating characteristics of different airport components; (2) nonlinear congestion effects affecting most airport facilities; and (3) complex interactions between airport user flows and facilities, which means that decisions regarding various facilities are interrelated. Potential demand fluctuations in a deregulated aviation market, combined with various other uncertainties, add further dimensions to the airport capacity planning problem. The core of airport facility development is to determine the timing and sizing of facility expansion projects.

The traditional airport master planning has been criticized for its limited ability to cope with future uncertainties. Although there are several general procedures and frameworks for improving the planning flexibility or adaptability in uncertain environments, these macro analyses are considered only conceptually useful and cannot generate detailed plans for implementation. Very few relevant studies are found and all of them focus on a single component (e.g., passenger terminal) or specific facility. However, an airport is a system of many components, which can operate in parallel or in series. In airport development, it is desirable to roughly equalize the capacities of facilities operating in-series. Therefore, the present work is distinguished by the design of global planning models which can coordinate the development of various components under several sources of uncertainties.

Due to the intricacy of the airport facility development problem, this dissertation presents a series of applied decision tools sequentially. Practical considerations, such as economies of scale, future cost discounting, nonlinear congestion, and project implementation time requirement, are captured in proposed optimization models which combine the difficulty of optimizing over binary variables and handling nonlinear relations. After examining the structural properties of optimization models, some simplification techniques are proposed, such as the out-approximation and discrete-approximation linearization methods, for enhancing solution efficiency and quality. Computational experiments demonstrate the benefits of such models. For instance, the total cost could be reduced significantly (e.g., by 18.8% in one test) with the proposed stochastic model, compared with decisions based on the average conditions. The decision tools developed can augment the airport master planning process in its ability to address future uncertainties. This work also offers methodological contributions in the field of infrastructure development, such as modeling of complex facility performances and a method for coordinating the development of various types of facilities.