Optimization Method for Area Controller Scheduling Integrating Efficiency, Fatigue and Fairness

Abstract

Abstract To address the problem that existing scheduling methods for area air traffic controllers cannot effectively coordinate and optimize operational efficiency, fatigue risk, and working time fairness, this paper proposes a collaborative optimization method based on the multi-objective marine predator algorithm (MOMPA). First, a multi-objective optimization model integrating efficiency adaptation, fatigue control, and fair allocation is constructed. Operational efficiency is quantified as capacity supply variables, fatigue risk is decomposed into two components—duration accumulation and intensity accumulation, and fairness is characterized by the dispersion of working hours. Hard constraints such as position coverage, qualification matching, and upper limits on consecutive duty hours are also incorporated. Second, a solution framework based on constraint programming satisfiability (CP-SAT) and MOMPA is proposed, where a constraint programming solver is used to generate strictly feasible schedules and achieve minimal disturbance repairs, while the multi-objective marine predator algorithm is employed to search for the Pareto front. Finally, the proposed method is validated using actual operational data from a regional control center in China. The results show that MOMPA significantly outperforms the improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) in terms of HV, IGD, Spacing, and other metrics, with lower computational cost. The optimized schedules achieve precise capacity supply that matches high demand with high allocation and low demand with compliant configurations, compress the right tail of fatigue distribution, and significantly reduce the proportion of extreme working hours. The recommended solution strikes a balance among efficiency, fatigue, and fairness, demonstrating good interpretability and engineering feasibility.