Shahram Shahinpour  |  Sabre

Traditional airline fleet assignment models (FAMs) often rely on leg-based demand and disregard complex network interactions, which limits their ability to maximize the total profitability of a schedule. To address market needs and remain competitive, airlines are increasingly interested in optimizing capacity allocation at a granular level, driven by passenger demand and fares for individual cabin classes. In this talk, we present a cabin-level, origin-and-destination fleet assignment model that overcomes the shortcomings of traditional FAMs and empowers airlines to optimize their capacity using detailed itinerary information.

Ahmed Abdelghany |  Embry-Riddle Aeronautical University

This study introduces an airline network optimization framework that treats passenger demand as endogenous to seat capacity allocation decisions. Empirical analysis indicates that Origin–Destination (OD) demand responds nonlinearly to capacity additions, following market-specific saturation curves represented by asymptotic exponential or stepwise functions. In this setting, demand influences capacity allocation decisions, while capacity simultaneously stimulates demand, creating a bidirectional endogenous relationship. The proposed model determines optimal weekly aircraft and seat allocations across OD markets while enforcing aircraft flow balance constraints throughout the network. Assuming fixed fares and known operating costs, the objective is to maximize network profit through coordinated fleet deployment, flight frequency assignment, and demand-responsive capacity planning.

Jeff Oboy | PA Consulting

Power-by-the-hour (PBH) is one of the most recognised and adopted concepts in aircraft engine maintenance. For years PBH agreements have alleviated significant operational burdens traditionally faced by airline powerplant managers while finance teams have welcomed the benefits of improved risk and budget management. However, PBH has come to foster a degree of complacency and contributed to misconceptions about an airline's influence and decision-making authority. This talk aims to provide airline OR teams with a clearer understanding of PBH and such's implications for decisions about when, where, and how engines can be most optimally serviced.

Narges Sereshti  | Air Canada

Airline schedule design requires balancing operational efficiency with robustness under uncertainty across large-scale, highly constrained networks. We present an integrated framework combining predictive modeling, discrete-event simulation, and large-scale optimization to improve schedule resilience. In the first phase, supervised learning models and a simulation engine are used to estimate delay propagation risk and identify structurally vulnerable connections within the flight network. These outputs are translated into quantitative penalties and robustness indicators that inform downstream optimization. The second phase consists of a time-space network optimization model for aircraft routing, formulated to minimize a composite objective including propagated delays, passenger misconnections, and operational inefficiencies (e.g., towing). The model explicitly enforces maintenance requirements, fleet compatibility, and turnaround constraints. Due to the scale of the problem—millions of decision variables and constraints—we employ graph-based preprocessing, and decomposition techniques to ensure computational tractability. This work demonstrates how simulation-informed parameters can be embedded within a deterministic optimization framework to better capture and resolve operational risk. The resulting approach enables more robust schedule solutions while maintaining feasibility within real-world airline constraints.