Prescriptive analytics in airline operations: Arrival time prediction and cost index optimization for short-haul flights

In this paper, we provide arrival time prediction combined with a cost index optimization model for short haul flights. Our work is based on flight data of a European network carrier. We focus on predicting the arrival time for incoming flights at two hub locations. Airlines focus on two aspects in their operations: Minimizing cost while ensuring on-time arrivals. Especially network carriers with hub connections need to ensure that incoming flights are on time for passenger, crew and aircraft transfer. The cost index is a tool for optimizing the aircraft’s speed. A high cost index implies a faster flight. The cost of time is set in relation to the cost of fuel. Today there is no model for arrival time prediction and integrated cost index optimization. We consider three different flight distances to model the impact of cost index changes on gate arrival time. With our model airlines are able to reduce the cost index without any tangible impact on their overall schedule. We conclude that the optimal cost index level heavily depends on a flight’s distance, fuel costs and delay costs. Especially for short haul flights we recommend lowering the cost index as a high cost index has limited impact on gate arrival time. Keywords: OR in airlines, Analytics, Aircraft arrival time prediction, Prescriptive analytics, Cost index optimizatio