[Paper] Optimisation of Aircraft Maintenance Schedules

Source: arXiv - 2512.17412v1

Overview

The paper tackles a real‑world logistics challenge: creating optimal maintenance schedules for aircraft while respecting crew qualifications, task dependencies, and tight turnaround windows. By applying an Evolutionary Algorithm (EA), the authors demonstrate that meta‑heuristic search can produce high‑quality schedules far faster than exhaustive methods—an insight that matters to airlines, MRO (maintenance, repair, and overhaul) providers, and any operation that must juggle complex resource constraints.

Key Contributions

• Formal definition of the aircraft maintenance scheduling problem that incorporates staff qualification, task precedence, and turnaround time limits.
• Design of a custom EA representation (chromosome encoding) that captures both aircraft‑task assignments and crew allocation in a single genotype.
• Development of problem‑specific genetic operators (crossover, mutation, repair) that maintain feasibility with respect to qualifications and time windows.
• Comprehensive benchmarking on 60 synthetic instances, showing the EA’s ability to find near‑optimal solutions within practical runtimes.
• Analysis of solution quality vs. computational effort, providing a baseline for future algorithmic improvements.

Methodology

1. Problem Modeling – Each aircraft has a set of maintenance tasks, each requiring a specific skill level and a known duration. The tasks must be sequenced within a fixed turnaround window (e.g., 3‑4 hours).
2. Chromosome Design – A chromosome is a concatenated list where each gene encodes a (task, assigned staff) pair. The ordering of genes implicitly defines the execution sequence.
3. Fitness Function – The algorithm evaluates a schedule by (a) total turnaround time (penalizing overruns), (b) qualification violations (heavy penalties), and (c) crew workload balance. Lower fitness values indicate better schedules.
4. Genetic Operators –
   • Crossover: a two‑point crossover swaps subsequences between two parent schedules, followed by a repair step that re‑assigns any orphaned tasks to qualified staff.
   • Mutation: randomly re‑assigns a staff member to a task or swaps the order of two tasks, again invoking repair to keep the schedule feasible.
5. Evolutionary Loop – Starting from a randomly generated population, the EA iteratively selects the fittest individuals, applies crossover/mutation, and replaces the worst performers. The process stops after a fixed number of generations or when improvement stalls.
6. Benchmark Generation – 60 problem instances of varying size (5‑20 aircraft, 10‑40 tasks) were synthetically generated to test scalability.

Results & Findings

• Solution Quality – Across all instances, the EA achieved an average turnaround time within 5 % of a lower bound obtained from a mixed‑integer programming (MIP) model, while respecting all qualification constraints.
• Runtime – Typical runs completed in under 30 seconds for the largest instances (20 aircraft, 40 tasks), a stark contrast to the hours required for exact MIP solvers.
• Scalability – Performance degradation was gradual; doubling the number of tasks increased runtime by roughly 1.8×, indicating good scalability for medium‑size fleets.
• Operator Effectiveness – The custom repair mechanism was crucial; without it, infeasible schedules rose to >30 % of the population, dramatically slowing convergence.

Practical Implications

• Airline Operations – Deploying an EA‑based scheduler can shrink turnaround windows, increasing aircraft utilization and revenue without hiring additional staff.
• MRO Planning Tools – Integration into existing maintenance management systems (e.g., AMOS, Ramco) could provide on‑the‑fly schedule adjustments when unexpected delays occur.
• Developer Takeaway – The paper offers a reusable EA framework (encoding + operators) that can be adapted to other resource‑constrained scheduling domains such as data‑center maintenance, shipyard repairs, or even software release pipelines.
• Cost Savings – By automating the allocation of qualified technicians, airlines can reduce overtime and improve compliance with regulatory maintenance windows, directly impacting operational cost structures.

Limitations & Future Work

• Synthetic Data – All experiments used generated instances; real‑world data (with stochastic delays, crew shift patterns, and regulatory constraints) may expose hidden complexities.
• Single‑Objective Focus – The current fitness function optimizes turnaround time; multi‑objective extensions (e.g., minimizing crew overtime, balancing skill development) are left for future research.
• Hybrid Approaches – The authors suggest combining the EA with exact methods (e.g., using EA solutions as warm‑starts for MIP solvers) to tighten optimality guarantees.
• Dynamic Re‑Scheduling – Extending the algorithm to handle real‑time disruptions (e.g., last‑minute flight cancellations) remains an open challenge.

Bottom line: This study proves that evolutionary meta‑heuristics are not just academic curiosities—they can deliver fast, high‑quality aircraft maintenance schedules that translate into tangible operational gains for airlines and MRO providers. Developers interested in optimization can borrow the encoding and operator designs as a solid starting point for tackling similarly constrained scheduling problems.

Authors

• Neil Urquhart
• Amir Rahimi
• Efstathios‑Al. Tingas

Paper Information

• arXiv ID: 2512.17412v1
• Categories: cs.NE, cs.AI
• Published: December 19, 2025
• PDF: Download PDF