Airline crew scheduling with sustainability enhancement by data analytics under circular economy

As an energy-intensive industry, it is critical for airlines to enhance operation sustainability under the circular economy. Airline crew pairing problem is to construct job itineraries. Traditionally, crew pairings are developed based on pre-determined flight schedules. That is, flight departure times, arrival times, and flying times are considered to be fixed according to the schedule. However, analytics on historical data reveal that the actual flight duration often varies according to the actual departure time, which may lead to a deviation of the actual arrival time from the scheduled time point. Thus, propagated effects are generated as the departure time and flying time of the next flight are also affected. Aircraft energy research has revealed that the fuel consumptions and greenhouse gas emissions of aircraft are affected by the actual flying speed and flight duration. Therefore, it is crucial to consider sustainability cost factors (i.e., fuel consumptions and greenhouse gas emissions) when building crew pairings. In this work, in order to enhance operation sustainability and promote circular economy, we propose a novel crew pairing problem which aims to minimize the total basic operation cost, the total fuel consumptions and greenhouse gas emissions, and the robustness cost of the generated pairings. A column generation based solution algorithm is developed. Computational experiments show that the proposed model can bring a 7.98% decrease in the sustainability cost and an 1.81% decline in the robustness cost with only 0.55% increase in the basic operation cost when all the three cost factors are with equal weightings.