Passenger-Centric Multi-Airport Seasonal Slot Allocation: An Optimization Model for Seasonal Slot Allocation.

This thesis presents the Passenger-Centric Multi-Airport Slot Scheduling Allocation model (PC-MASSA), a novel stochastic optimization framework that integrates passenger demand into airport slot allocation. Unlike traditional models that focus mainly on airline operational constraints, PC-MASSA incorporates passenger priorities: business, frequent flyers, medical, and leisure, using a hybrid forecasting approach combining gravity models and machine learning. The model applies a two-stage optimization: a Mixed-Integer Linear Programming (MILP) formulation generates an initial solution balancing passenger satisfaction, airline efficiency, and regulatory compliance, while a heuristic refinement addresses demand uncertainty. Using flight data from the OpenSky Network and passenger statistics from Statista and European airports, PC-MASSA aims to enhance efficiency, reduce layovers, and adapt capacity dynamically, while ensuring fairness for both passengers and airlines. A case study on the Paris multi-airport system demonstrates the model’s practical advantages in improving efficiency, responsiveness, and equity in slot allocation under real-world conditions.