The Tail Assignment Problem (TAP) is a key subcomponent in airline scheduling, involving the allocation of specific aircraft (“tails”) to flights while satisfying operational, maintenance, and logistical constraints. Due to the high combinatorial complexity of TAP, we explore a quantum-ready formulation based on Quadratic Unconstrained Binary Optimization (QUBO), designed to leverage quantum annealing and future quantum hardware. This model encodes both hard constraints and operational cost functions into a unified optimization framework. We complement quantum-inspired approaches with classical optimization methods and heuristic techniques to ensure practical applicability and robustness. These include decomposition by aircraft family, time-windowed scheduling, and resilience-enhancing strategies such as slack time and recovery potential.