A Multi-strategy Enhanced Memetic Algorithm for Airport Refueling Vehicle Scheduling Problem

Recently, building intelligent and efficient airports has been demanding. Airport refueling vehicles are required to serve flights with time constraints efficiently. The airport refueling vehicle scheduling problem is a typical vehicle routing problem with time windows (VRPTW), which is defined as a problem designing least-cost routes for refueling vehicles to serve distribution points with time window constraints. In this paper, a multi-strategy enhanced memetic algorithm (MSEMA) is presented to solve VRPTW. Firstly, a greedy initialization method is desigined to optimize the search ability of MSEMA in early iterations. Then, MSEMA employs the crossover and simulated annealing based adaptive mutation to increase the diversity of the population. Moreover, a hybrid local search strategy is designed to improve the convergence fitness value. Experiments indicate that, compared to other hybrid algorithms, MSEMA shows better convergence fitness values and faster convergence speed in early iterations on different problem scales. Besides, in the ablation study, the hybrid local search strategy showed its effectiveness in improving the local search ability of MHEMA.