A Stepwise Enhanced Genetic Algorithm for Aircraft Maintenance Personnel Scheduling Problems

To make every engineer maintain as many aircraft types as possible in a short time period, we design a comprehensive management framework for optimizing a personnel scheduling problem including a data set, a set of constraint conditions and a new optimization algorithm. In addition, a data set describing information about engineers and aircraft for further study is provided. A stepwise enhanced genetic algorithm (GA) is proposed to improve the performance of conventional GA under defined constraints. In the enhanced GA, a series of improvement measures include designing initialization operations based on greedy principles and selecting initial points, an adaptive combination fitness function roulette selection operator, a three-parent bidirectional heuristic crossover operator, and a combination mutation operator that can adaptively adjust are designed and effectively mixed for use. The enhanced GA is verified using the empirical data and the results show better performance than the conventional GA.