In this work with Jelly Ziyue Li and Qianwen (Vivian) Guo at FAMU-FSU College of Engineering, we explored the use of analytical real options models to manage capacity expansion of airport runways under uncertainty. The proposed system modeling framework includes “jump diffusion processes” that account for disruptions in system capacities. While both conventional reinforcement learning and analytical real options models deal with optimal policies, the latter assumes underlying variables follow simple stochastic processes, ie random distributions over time, and derive optimal threshold policies (e.g. if demand variable exceeds threshold H, execute decision). The approach makes it easy to quantify the impacts of uncertainty on timing of strategic decisions. (e.g. disruption rates increased 10%? that may be reflected by an increase in the value of having the option to make the decision in the future).

Paper link: https://www.sciencedirect.com/science/article/abs/pii/S0969699725000870?dgcid=coauthor