Stability-Aware Constraint Programming for Emergency Rescheduling of Airport Apron Ground-Support Operations

Short-horizon emergency re-optimization of airport apron ground-support operations works when its objective carries timing stability, and backfires when it does not. Using a new benchmark—89 days of Amsterdam and Chicago ADS-B ground traces including transponder-equipped ramp service vehicles, with semi-synthetic task and fault layers: 3540 counterfactual-controlled fault episodes and 740 test reschedule instances with CP-SAT references (85% proved optimal)—we score every recovery over the 30–120 min emergency horizon using a full-day shadow replay against a matched no-fault counterfactual, with date-level paired inference. CP-SAT with a start-time stability term, or with a lexicographic rule that returns the most plan-preserving plan attaining the first-stage window objective value, matches plan-preserving repair on realized disruption on vehicle-removal faults (median 1.6–2.4 vs. 2.7 ks) and lowers absolute realized tardiness on 19/19 test dates. The same model minimizing only in-window weighted tardiness is worse end-to-end than greedy earliest-due-date dispatch on 19/19 dates (exact p=3.8×10−6). For instances certified as optimal at every churn budget, the unconstrained solve reaches the window optimum of the minimal-churn solve yet realizes 2.6 times more disruption; learned re-optimizers trained on the same objective are worse still, and rolling-horizon repetition amplifies the harm. The harm travels through re-timing and re-sequencing rather than reassignment, so an assignment-only churn penalty does not help. The typical instance has no in-window headroom over greedy dispatch (median learning space 0). The ordering holds across ten generator settings and service-time noise. The benchmark will be made publicly available.