A data-driven air cargo redistribution model based on multiple programming

Abstract

Air cargo transportation is an essential mode of cargo transportation. How to distribute air cargo into flights better is an important operational problem. In this paper, air cargo data collected by CAAC (Civil Aviation Administration of China) during the first three months of 2018 are analyzed. We find that the available capacity for cargo transportation shows great variations, and the cargo compartment utilization rates of flights are heterogeneously distributed. Next, a data-driven air cargo redistribution model is developed based on multiple programming (MP). The proposed model can effectively transport high-priority goods and balance cargo compartment utilization rates of flights. In addition, the proposed model framework can provide a new solution to multi-objective or multi-stage optimization problems.