Citation: Zhang, J.; Chong, X.; Wei,
Y.; Bi, Z.; Yu, Q. Optimization of
Apron Support Vehicle Operation
Scheduling Based on Multi-Layer
Coding Genetic Algorithm. Appl. Sci.
2022, 12, 5279. https://doi.org/
10.3390/app12105279
Academic Editors: Phivos Mylonas,
Katia Lida Kermanidis and
Manolis Maragoudakis
Received: 14 April 2022
Accepted: 18 May 2022
Published: 23 May 2022
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Article
Optimization of Apron Support Vehicle Operation Scheduling
Based on Multi-Layer Coding Genetic Algorithm
Jichao Zhang
1,
* , Xiaolei Chong
1
, Yazhi Wei
1
, Zheng Bi
2
and Qingkun Yu
1
1
Aviation Engineering School, Air Force Engineering University, Xi’an 710038, China;
chongxiaolei20@163.com (X.C.); weiyazhi814@163.com (Y.W.); yuqingkun111@163.com (Q.Y.)
2
Western Theater Air Force Survey and Design Institute, Chengdu 610000, China; bizheng111@163.com
* Correspondence: zhangjichao930@163.com
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.
Keywords:
support vehicles operation scheduling; multi-layer coding genetic algorithm; time opti-
mization; utilization rate; safety assurance capability
1. Introduction
Operation scheduling of apron support vehicles is an important support activity
at the direct preparation stage of aircraft support. The aircraft support activities at this
stage should be completed within the last 30 min before the flight time. Furthermore, the
maintenance and support capability of the aircraft on the apron is related to the efficiency
of the aircraft’s re-deployment [1,2].
The traditional apron support vehicle operation process is derived from the stand-
alone preparation process, which is formulated by the support unit based on the daily
support experience. This guarantee model can accurately guarantee each aircraft, reduce
guarantee errors, and ensure guarantee quality. However, the traditional support mode
often results in delayed aircraft support or long-time vacancy of the vehicle [
3
]; furthermore,
the lack of a thorough scheduling plan causes the consequences of low support efficiency
and waste of support resources [4,5].
In this case, people are accustomed to taking the ground dispatch service of the airport
as a Vehicle Routing Problem with Time Windows (VRPTW) [
6
,
7
] by considering the
utilization of the vehicle reuse rate, the minimum used vehicle or the constraints, such as
parallel services to analyze the problem, and an ant colony optimization algorithm [
8
,
9
],
genetic algorithm (GA) [
10
,
11
], greedy algorithm [
12
], or multi-objective optimization
algorithm [
13
] to solve the problem of guaranteed vehicles scheduling. Solving the vehicle
routing problem can guarantee the shortest driving path of the vehicle, thereby reducing the
Appl. Sci. 2022, 12, 5279. https://doi.org/10.3390/app12105279 https://www.mdpi.com/journal/applsci
