Integrated airline fleet introduction and assignment under a daily route-based network

This study investigates an integrated airline fleet introduction and assignment problem under a daily route-based network. It is critical for an airline to integrate advanced digital technology with the methodology of operational research to achieve smart operations. An integer programming model is proposed for this problem. Based on the model, a hybrid algorithm combining a multi-encoding variable neighborhood search (VNS) and Branch-and-Bound (B B) method is proposed. Firstly, the multi-encoding VNS solves fleet introduction and determines fleet numbers. A self-adaptive multi-layer encoding process with nine neighborhood structures is designed in the VNS process to improve the local search efficiency. Secondly, given the fleet numbers, the improved B B method driven by profit matrix is designed to solve fleet assignment. There are three modified strategies in the B B method containing cyclic best-first search, wide branching, and upper bound pruning. Finally, computational experiments are conducted to test algorithm performance. The proposed algorithm is compared with basic VNS, differential evolution, and genetic algorithm on objective value, running speed, and stability. The experiments show that the proposed model and algorithm offer a smart methodology for airline integrated problem.