A Novel Robust Optimization Model for Airline Crew Scheduling and Rostering

Abstract

Airline crew scheduling is one of the most complex operational planning problems in the aviation industry due to the large number of flights, strict regulatory constraints, and operational cost considerations. Crew expenses typically represent the second-largest operational cost component for airlines after fuel consumption. Efficient crew scheduling is therefore essential for maintaining airline profitability and operational efficiency.
This study proposes an advanced hybrid optimization framework for airline crew scheduling that integrates mathematical programming models with metaheuristic search algorithms. The proposed model addresses both the crew pairing and crew rostering stages while ensuring compliance with aviation safety regulations, duty time limitations, rest requirements, and crew qualification constraints. A mathematical formulation based on binary decision variables is developed to minimize the total operational cost associated with crew assignments.
To handle large-scale airline networks, the model incorporates evolutionary search strategies that improve computational efficiency and solution quality. A simulation case study involving a representative airline network is conducted to evaluate the performance of the proposed model. The experimental results demonstrate significant improvements in crew utilization, scheduling cost reduction, and operational efficiency compared with traditional scheduling methods.
The findings highlight the potential of advanced optimization models in improving airline resource allocation and operational planning. The proposed framework can support airline decision-making systems and contribute to more efficient and sustainable airline operations.