²ÝÝ®ÊÓƵ

This deterministic model was validated using an example case of the Sydney metropolitan area in Australia, which presented an optimal solution of a dual airport system along with scalable outcomes for a 50-year timeline. The study also tested alternative input values to the discount rate, operation cost, and passenger access costs to review the reliability of the deterministic model. Six additional experimental models were tested, and all models successfully yielded optimal solutions. The moderating effects of financial discount rate, airport operation cost, and passenger access costs on the optimal solution were quantitatively the same in presence of a deterministic demand profile.

This deterministic model was then transformed into a stochastic optimization model to address concerns with the uncertainty of future traffic demand, which was further reviewed with three what-if demand scenarios of the Sydney Model: random and positive growth of traffic demand, normal distribution of traffic demand changes based on the historical traffic record of the Sydney region, and reflection of the current COVID- 19 pandemic situation. This study used a Monte Carlo simulation to address the uncertainty of future traffic demand as an uncontrollable input. The Sydney Model and three What-if Models successfully presented objective model outcomes and identified the optimal solutions to expand airport capacity while minimizing overall costs. The results of this work indicated that the moderating effect of traffic uncertainties can make a difference with an optimal solution. Therefore, airport decision-makers and airport planners should carefully consider the uncertainty factors that would influence the airport capacity expansion solution.

This research demonstrated the effectiveness of combining MINLP and the Monte Carlo simulation to support a long-term strategic decision for airport capacity problems in metropolitan areas at the early stages of the planning process while addressing future traffic demand uncertainty. Other uncertainty factors, such as political events, new technologies, alternative modes of transport, financial crisis, technological innovation, and demographic changes might also be treated as uncontrollable variables to augment this optimization model.