  Freighter airlines need to recover both aircraft and cargo schedules when
disruptions happen. This process is usually divided into three sequential
decisions to recovery flights, aircraft, and cargoes. This study focuses on the
integrated recovery problem that makes aircraft and cargo recovery decisions
simultaneously. We formulate two integrated models based on the flight
connection network, one is the arc-based model, and the other is the
string-based model. The arc-based model makes the flight delay decisions by
duplicating flight copies, and is solved directly by commercial solvers such as
Cplex. The string-based model makes the flight delay decisions in the variable
generation process. The main difficulty of the string-based model is that the
number of constraints grows with the newly generated flight delay decisions.
Therefore, the traditional column generation method can not be applied
directly. To tackle this challenge, we propose a machine learning based
column-and-row generation approach. The machine learning method is used to
uncover the critical delay decisions of short through connections in each
column-and-row generation iteration by eliminating the poor flight delay
decisions. We also propose a set of valid inequality constraints which can
greatly improve the objective of LP relaxation solution and reduce the integral
gap. The effectiveness and efficiency of our model is tested by simulated
scenarios based on real operational data from the largest Chinese freighter
airlines. The computational results show that a significant cost reduction can
be achieved with the proposed string-based model in reasonable time.
