Robust modeling for fleet assignment problem based on GASVR forecast

Abstract

Fleet assignment is the essential step of overall airline flight cheduling process, and the quality of assignment strategy directly affects economy and safety of air transportation. Market demand forecast is the premise of fleet assignment, and accurate forecast is an important guarantee to reduce passenger overflow and improve aircraft utilization rate. In order to carry out scientific fleet assignment, this paper studies from two aspects: Firstly, support vector machine regression (SVR) is used to forecast flight passenger flow and solve the problem of undeterminable parameters, we present a GA-SVR model with genetic algorithm for parameter optimization. Secondly, from the perspective of flight recovery efficiency, this paper incorporates the concept of fleet robustness, and establishes the robust model for fleet assignment with dual-objective, which maximizes the flight operating profit and minimizes the number of aircraft type in busy airports. Finally, flight network of an airline is analysed to verify the validity of the model and algorithm. It shows that: the MSE mean value of GA-SVR prediction results is between 0.0103 and-0.0031, which is relatively accurate. And the fleet assignment model can significantly improve robustness (16.7%) at the expense of less profit (4.2%).