Optimal Assignment of Cabin Crew in Airlines

Abstract

The aviation industry faces complex challenges in efficiently managing cabin crew assignments while ensuring operational efficiency, regulatory compliance, and high service quality. This project aims to develop a systematic approach to optimize cabin crew allocation using the Hungarian method, with Indigo Airlines as the primary case study. The project focuses on developing a mathematical optimization model that incorporates critical factors, including flight schedules, duty time limitations, and mandatory rest periods. The Hungarian algorithm is applied to operational data from Indigo Airlines, with variables such as crew base locations, flight durations, sector pairings, and turnaround times. By formulating the crew assignment problem as an assignment problem, the project provides an effective and scalable solution to minimize overall costs. The impact on operational efficiency is analyzed through key performance indicators such as crew pairing costs, resource utilization, and the optimization of crew workloads. Additionally, the project explores the trade-offs between cost reduction, crew satisfaction, and regulatory adherence, aiming to provide a balanced solution that enhances both operational performance and crew morale. Ultimately, this project contributes to the field of airline operations by offering a practical, implementable solution for cabin crew assignment optimization, providing airlines with a tool to enhance productivity, reduce operational costs, and improve overall service quality.

Introduction

Crew scheduling in airlines has grown increasingly complex due to rising air traffic, global connectivity, and stricter regulations. Major international airlines and airports face challenges balancing operational efficiency and crew well-being, with inefficient scheduling accounting for over 15% of operational costs. Scheduling must comply with regulations on crew rest and handle real-time disruptions like weather or technical issues. Traditional manual or semi-automated systems lack flexibility, prompting the exploration of more effective algorithms.

The text defines key scheduling terms such as air legs, duties, layovers, and pairings, essential for understanding crew assignments. Efficient crew scheduling impacts airline performance by improving customer satisfaction, reducing delays, ensuring regulatory compliance, and optimizing labor costs.

Literature review highlights traditional and modern methods including integer programming, genetic algorithms, and simulation, noting the Hungarian Algorithm as a particularly efficient and simple solution for assignment problems. It guarantees an optimal solution in polynomial time and is faster and easier to implement compared to other methods.

The problem is formulated as assigning each crew member to one flight to minimize costs related to travel distance, rest violations, and idle time, under constraints like crew availability and mandatory rest. An example dataset illustrates how the cost matrix is built and used.

The Hungarian Algorithm’s steps are detailed: row and column reduction, covering zeros with minimal lines, adjusting the matrix, and finding optimal assignments.

Finally, the algorithm is applied to a real-world crew scheduling scenario involving flights between Delhi and Jaipur, focusing on minimizing layover times and efficiently assigning crews to flights, demonstrated through cost matrices and reductions.

Conclusion

This research presents a systematic approach to cabin crew scheduling using the Hungarian Algorithm, aiming to minimize operational costs and crew downtime. By formulating the problem as a cost minimization assignment model, we introduced practical weight factors like flight duration, rest requirements, and time constraints into the cost matrix. A real-world dataset was used to validate the model, highlighting its ability to provide optimal and constraint-compliant assignments efficiently.

References

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Copyright

Copyright © 2025 Pinky Goyal, Purnima Singh, Rana Vishal Singh, Sushant Singh, Mr. Shivanshu Shahi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.