Case Study: Scheduling & Recovery

Optimizing Airline Flight Scheduling and Schedule Recovery

A major U.S. airline needed a more efficient flight scheduling and recovery system to manage thousands of daily flights while addressing unexpected disruptions. OSI developed an advanced optimization-based solution that improved scheduling efficiency, reduced customer impact, and enhanced operational resilience.

The Challenge:

• Complex Scheduling: Assigning aircraft efficiently while considering maintenance, crew availability, and regulatory constraints.
• Disruptions & Recovery: Rapidly responding to crew shortages, weather delays, and operational changes while minimizing passenger and airline impact.
• Scalability: Ensuring the solution could handle increasing flight volumes efficiently.

The Solution:

OSI collaborated with airline operations and IT teams to develop a tailored solution addressing data limitations, regulatory constraints, and operational challenges. Our approach included:

• Advanced Mathematical Formulation: We designed a custom optimization model that efficiently captured airline-specific constraints, minimizing the number of decision variables and constraints while ensuring practical feasibility.
• AI/ML-Enhanced Scheduling: We leveraged artificial intelligence and machine learning to generate high-quality initial solutions, which served as warm starts for a Mixed Integer Programming (MIP) solver. This significantly improved both solve time and solution quality.
• Industrial-Grade Implementation: Our solution was built using an industrial-strength MIP solver and deployed within the airline’s IT environment, ensuring seamless integration and ease of maintenance.
• Stress Testing & User Acceptance: The system was rigorously tested with various real- world disruption scenarios to validate its effectiveness. User acceptance testing confirmed its ability to handle a wide range of disruptions while maintaining operational efficiency.

Key Results & Benefits:

OSI’s solution delivered significant operational improvements, including:

• 30% Reduction in Solve Time – Faster optimization improved responsiveness to scheduling changes.
• 2% Increase in Operating Margins – More efficient flight assignments led to cost savings.
• 30% Reduction in Customer Impact – Fewer passengers were affected by disruptions, improving service reliability.
• 20% Reduction in Crew Tour Disruptions – Enhanced scheduling minimized crew reassignment disruptions.