Optimal Scheduling of Airport Refueling Vehicle Based on Improved NSGA - II

Abstract

In order to reduce overall costs and improve operational efficiency of airport refueling vehicle, an improved fast non-dominated multi-objective genetic algorithm is proposed. Firstly, more elite individuals are obtained by selecting elite parent individuals with different objective functions for mutation; secondly, the population quality is improved by deleting duplicate individuals of the offspring; finally, an adaptive crossover probability function based on the number of duplicate individuals is designed to improve merit-seeking ability. The effectiveness of the improved algorithm is verified by the actual operation data of an airport. Furthermore, after verified the effect of vehicle speed and load capacity, it is concluded that the scheme with a speed of 40 to 60 km/h and a load of 75% to 85% can reduce total cost without increasing vehicle waiting time at the airport.