Stability-Aware Constraint Programming for Emergency Rescheduling of Airport Apron Ground-Support Operations
- 1College of Air Traffic Management, Civil Aviation Flight University of China, Deyang 618311, China
Abstract
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.
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BibTeX
@article{huang2026stabilityaware,
title = {{Stability-Aware Constraint Programming for Emergency Rescheduling of Airport Apron Ground-Support Operations}},
author = {Tianyu Huang and Ke Tang and Youyou Li},
journal = {Mathematics},
year = {2026},
month = oct,
doi = {10.3390/math14203778},
url = {https://www.mdpi.com/2227-7390/14/20/3778},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Stability-Aware Constraint Programming for Emergency Rescheduling of Airport Apron Ground-Support Operations
AU - Tianyu Huang
AU - Ke Tang
AU - Youyou Li
PY - 2026
DA - 2026/10/18
JO - Mathematics
DO - 10.3390/math14203778
UR - https://www.mdpi.com/2227-7390/14/20/3778
AB - 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.
SN - 2227-7390
ER -