Optimizing backup crew planning in airlines using machine learning

Abstract

Due to the inherent nature of the aviation sector, it entails a highly challenging and complicated operational process. There are many internal and external factors that affect operational processes. All process steps are optimized by addressing them as separate challenges. Crew planning, which is also a significant part of operations and ranks second in terms of expenses, plays a crucial role in maximizing revenue and minimizing costs. Crew planning in the aviation industry is complex, made even more challenging by external factors like weather changes, crowded airspace, and unexpected technical problems. Planning flights, scheduling crews, and matching them with each other are considered as separate difficult problems. Additionaly, ensuring efficient communication and coordination between these components further complicates the task. Taking precautions against disruptions in these processes is essential to prevent them from affecting the entire flight and operation process. Moreover, implementing contingency plans and backup strategies can help minimize the impact of unexpected events, ensuring smoother operations and maintaining overall efficiency in aviation management. Adaptability and quick response are vital in this dynamic environment. In these processes where dynamism and fast problem-solving are critical, additional efforts are required to manage the team effectively. In this regard, reserved crew planning comes into play, which is essential for the smooth management of unplanned flights and operational disruptions. Reserved crew planning is necessary for unplanned flight and operation disruptions. As crew absence can impact the entire flight process, it's crucial to optimize the number of crew members to be kept on reserved daily. The optimization of the reserved crew is addressed using solution methos for crew assignment problem. Using advanced analytics and machine learning, airlines can better understand operational trends and make smarter decisions on the fly. This research explores how these solutions can enhance reserved crew planning. Experiments were conducted on a classification problem using various machine learning models such as K-Nearest Neighbors (KNN), Random Forest, XGBoost, and Logistic Regression. The performance of each model was compared using various evaluation criteria.