A machine learning based column-and-row generation approach for integrated air cargo recovery problem

Freighter airlines need to recover both aircraft and cargo schedules when disruptions happen. This process is usually divided into three sequential decisions to recover flights, aircraft, and cargoes. This study focuses on the integrated recovery problem that makes aircraft and cargo recovery decisions simultaneously. We formulate a string-based model to solve the integrated air cargo recovery problem. The main difficulty of the string-based model is that the number of constraints grows with the newly generated flight delay decisions in the variable generation subproblem. 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 that can greatly improve the objective of LP relaxation solution and reduce the integral gap. The effectiveness and efficiency of our model are tested by simulated scenarios based on real operational data from the largest Chinese freighter airline. The computational results show that a significant cost reduction can be achieved with the proposed integrated model in a reasonable time.