Optimization of Apron Support Vehicle Operation Scheduling Based on Multi-Layer Coding Genetic Algorithm

Abstract

Operation scheduling of apron support vehicles is an important factor affecting aircraft support capability. However, at present, the traditional support methods have the problems of low utilization rate of support vehicles and low support efficiency in multi-aircraft support. In this paper, a vehicle scheduling model is constructed, and a multi-layer coding genetic algorithm is designed to solve the vehicle scheduling problem. In this paper, the apron support vehicle operation scheduling problem is regarded as a Resource-Constrained Project Scheduling Problem (RCPSP), and the support vehicles and their support procedures are adjusted via the sequential sorting method to achieve the optimization goals of shortening the support time and improving the vehicle utilization rate. Based on a specific example, the job scheduling before and after the optimization of the number of support vehicles is simulated using a multi-layer coding genetic algorithm. The results show that compared with the traditional support scheme, the vehicle scheduling time optimized via the multi-layer coding genetic algorithm is obviously shortened; after the number of vehicles is optimized, the support time is further shortened and the average utilization rate of vehicles is improved. Finally, the optimized apron support vehicle number configuration and the best scheduling scheme are given.

Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling

“…Recently, Zhang et al used GA to solve the scheduling problem of supporting vehicles, which is similar to AGH but ignores the travel time of vehicles between aircraft. The method was evaluated with less than 10 aircraft, which is far from the ones in practical situations [15].…”

“…The number of vehicles in each fleet is randomly sampled from [10,20]. We randomly generate the flight demands for each operation from [5,15], and set the ratio of demand to capacity to a random value in [0.7, 0.9] following [45].…”

Analysis Service Process of Aircraft Turnaround Operations by Petri Net

“…According to a European research report in 2019, 33% of flight departure delays are caused by ground services [1] . In order to improve efficiency, researchers have mainly conducted the following studies in recent years: (1) Research on flight service resources scheduling on the ground, which mainly focuses on the establishment of a flight ground scheduling model [2][3][7][8][9] and the design of its solving algorithm [4][5][6][10][11][12] . (2) Research on accurate prediction methods of flight ground support service time [13][14][15][16] .…”