ABSTRACT The rapid growth in demand for air travel, coupled with the pursuit of optimal budgets and exposure to exceptional and irregular events, presents serious obstacles to long‐term airport capacity planning. Traditional planning methods, based on deterministic estimates, often underestimate unforeseen risks and can lead to non‐optimal investment decisions when demand deviates from expected trends. This work proposes a framework for optimizing airport capacity over multiple periods based on a multi‐objective mixed integer model, considering demand uncertainty and explicitly incorporating the conditional value at risk (CVaR) to control severe disruption losses. Both continuous capacity expansions and discrete infrastructure projects are incorporated into the model under strict annual budget constraints. Also, scenario‐based demand uncertainties, passenger redistribution between airports and penalties for failure to meet demand were considered. A weighted method is employed to convert the multi‐objective model to a single model and the model is solved using a commercial solution program in MATLAB. The proposed framework is utilized in a multi‐airport system in Saudi Arabia, comprising four major airports across three planning periods and eighteen demand scenarios, including baseline growth, high‐growth conditions and abrupt demand surges, intended to illustrate the tail of the CVaR curve. A systematic analysis of the objective weight configurations reveals clear shifts in optimal planning behavior as risk aversion increases. Cost‐focused solutions prioritize incremental capacity expansion but suffer significant unmet demand under extreme scenarios, while risk‐averse solutions allocate capacity more conservatively and significantly reduce tail losses at the expense of higher anticipated disruption costs. The results demonstrate the practical value of CVaR‐based planning in enhancing system resilience to rare but high‐impact demand shocks. The proposed approach provides decision‐makers with a transparent and flexible tool to balance efficiency and robustness in airport infrastructure investments, particularly in areas experiencing seasonal peaks and significant increases in demand driven by specific events.
Ibrahim M. Hezam (Thu,) studied this question.