MB9 - Applications in Aviation and Transportation

We introduce a compact optimization framework for a fuel delivery problem with heterogeneous multi-compartment vehicles with time windows. We propose a logic-based Benders decomposition with a compact trip generation scheme based on structural dominance. The approach reduces the solution space while preserving optimality, yielding improved computational performance.

Aircraft turnaround performance is managed through milestone timestamps rather than full task-level telemetry, creating a partially observed reliability problem. We develop a data-driven framework that extracts turnaround variants and precedence structures from A-CDM event logs via process mining, identifies delay-driving handoffs through Shapley-based attribution, and places minimal time buffers using chance-constrained and distributionally robust optimization with Wasserstein ambiguity sets. The robust formulations hinge against finite-sample error and distributional shift across operational regimes. Time-split back-testing on real airport data shows the approach meets on-time departure targets with significantly less slack than standard practices.

Airline ground staff scheduling is challenging given operational uncertainties such as correlated delays, fluctuating task times, and staff shortages. Traditional stochastic scheduling methods, such as stochastic programming, struggle to realistically capture task delays and dependencies. This study uses simulation-optimization (SimOpt) to better reflect real-life complexities. A discrete-event simulation mimics the arrivals and departures of aircraft, the baggage handling tasks, and the impact of delays, task duration uncertainties, and staff shortages. We apply and compare optimization methods, to develop shift-break schedules that handle daily operational uncertainties effectively. Results show that SimOpt provides more reliable decision support than traditional methods, leading to greater resilience and substantial cost savings in airline ground operations.

With global warming, Clear Air Turbulence (CAT) is expected to increase which represents a growing threat for flight safety. This study investigates air traffic optimization and proposes a column generation approach to mitigate CAT hazards, preserving flight safety and airspace capacity while supporting efficient and reliable air traffic operations.