Optimizing aircraft maintenance tasks allocation using mixed integer linear programming

This study addresses the optimization of aircraft maintenance task allocation for small fleets using Mixed-Integer Linear Programming (MILP). The research integrates manpower efficiency, regulatory compliance, and workload balancing to minimize downtime and enhance resource utilization. A mathematical model is formulated to account for task durations, skill levels, and sequential/parallel task constraints, validated via MATLAB implementation. Results from a simulated 50-hour Cessna 172 maintenance check demonstrate a 25% reduction in completion time compared to average manual scheduling. The model balances workloads by assigning tasks based on manpower expertise, highlighting the critical role of human factors in reducing errors and improving efficiency. Practical implications include a cost-effective alternative to commercial software for small operators, enabling optimized planning without high-cost tools. This work bridges a gap in maintenance literature by explicitly incorporating manpower efficiency into MILP frameworks, offering actionable insights for regulators and operators to avoid maintenance congestions and enhance operational resilience.