Aircraft Stand Pre-Assignment Under a Calibrated Occupancy Risk Model

We formalise aircraft stand pre-assignment as a constrained Markov decision process whose hard operational predicates (stand validity, size compatibility, buffered temporal non-overlap and adjacency exclusion) define a feasible action mask; we prove that any scoring policy composed with this mask assigns only feasible stands, and we derive in closed form the expected conflict minutes (ECM) accumulated by a schedule when occupancy durations follow calibrated piecewise-linear predictive distributions. In a preregistered one-shot replay of all 516 test days at San Francisco International Airport, a greedy policy that consumes this risk model cuts model-based expected conflict under the calibrated occupancy-risk model from 1175.5 to 615.9 min per 100 reservations relative to the strongest rule baseline: a 48% reduction (Holm-corrected p=3.0×10−13), with zero constraint violations and no deferrals. Three further preregistered hypotheses are reported not confirmed: graph-embedding and blended rankers reverse significantly, and masked discrete conservative Q-learning beats behaviour cloning but not the rule.