Strategic capacity planning framework for airport passenger terminal facilities.

Facility requirements describe how the capacity of a facility should be adjusted over time to meet the expected future demand levels. Practitioners use them to determine the strategic development of airport passenger terminal facilities. The generation of facility requirements is extraordinarily complex, since (i) airport strategic planning is subject to high levels of uncertainty due to the extremely long planning horizons considered, and (ii) investments in infrastructure are subject to irreversibility. This study presents a strategic capacity planning framework consisting of two modules, by means of which stochastically optimal facility requirements for airport passenger terminal facilities can be determined. The demand module is applied first. Its purpose is twofold: on the one hand, to create annual aggregated demand scenarios of an airport by means of geometric Brownian motion. On the other hand, to convert these scenarios into facility-specific design hour loads with the help of linear regression models. Subsequently, the capacity expansion problem module is used to determine conventional and flexible facility requirements that maximize the net present value of an airport passenger terminal facility. For this purpose, both conventional and flexible capacity expansion problem models, presented in the literature, are adapted to the needs of airport strategic planning. Subsequently, they are solved with evolutionary optimization algorithms. The framework is applied to a real-world planning example of the existing check- in facilities at Zurich Airport. The aim of the planning example is to compare flexible facility requirements with conventional facility requirements in terms of their economic value, and to investigate how sensitive the proposed models are to variations in several input factors. The results suggest that flexible facility requirements are generally more valuable than conventional facility requirements. Moreover, the models applied in this study respond to changes in input factors in a similar way to comparable models documented in the literature.