Akademik Veri Yönetim Sistemi
Journal of the Faculty of Engineering and Architecture of Gazi University, cilt.39, sa.4, ss.2031-2040, 2024 (SCI-Expanded)
With the increasing competition in the aviation industry in recentyears, airline companies have tended to manage theiro perations more efficiently. Airline scheduling activities are carried out in four stages as flight scheduling, air craft scheduling, crew scheduling and disruption management. In the first three stages, a feasible flight schedule is created for the system, and in the last stage, solutions are sought for the problems that arise during the flight. Airline companies face a serious loss of time and cost constraints in flight disruptions. The most difficult aspect of flight disruption management is the necessity of rescheduling the plans in minutes. When flight disruptions occur, in the case of rescheduling, companies make decisions based on traditional methods, in tuition or experience, and the excess of details in operations negatively affects the decision. In the study, in there scheduling process related to flight disruptions; machine learning algorithms were used to determine the risk factors, to easily access meaningful data and to make a delay prediction for flights to help decisionmaking. Machine learning has provided support to the decision maker in rescheduling by extracting meaningful new information from the current data, and flight delays have been predicted with classification algorithms.