Optimization Method for Aircraft Fleet Maintenance Support Personnel Allocation Based on Improved Genetic Algorithm

Currently, airport fleet maintenance is characterized by diverse aircraft models, densely concurrent tasks, and tightly coupled professional skills. Traditional specialized personnel allocation models often lead to severe human resource redundancy and high costs, whereas fully generalized models struggle to meet the strict safety and professional qualification requirements for complex maintenance. Scientifically allocating maintenance personnel to balance support costs and operational efficiency under the constraints of broad task scopes, varied personnel qualifications, and strict time limits has become a critical issue for ensuring stable fleet operations and overall airport efficiency. To address this issue, this study proposes an optimization method for aircraft fleet maintenance support personnel allocation based on an Improved Genetic Algorithm. First, the practical challenges are analyzed and a modeling framework is established by systematically examining the implementation processes, professional skill requirements, and time-consuming characteristics of common maintenance tasks across various aircraft models. Next, the minimum required number of maintenance personnel is calculated as a baseline constraint based on the man-hour method. Subsequently, incorporating practical engineering constraints such as cross-type aircraft support, an Improved Genetic Algorithm integrating adaptive crossover/mutation operators and an elite local hill-climbing strategy is designed to solve the allocation optimization problem. Finally, case studies under four allocation schemes and multiple ablation experiments are performed to comprehensively verify the reliability and rationality of the proposed method. The experimental results demonstrate that the optimal personnel allocation scheme obtained with this method saves approximately 10% to 11% in total human resource costs, when compared to the traditional independent support model. This study provides a scientific decision-making basis and technical support for the refined allocation of human resources in the context of fleet maintenance.