Study on Short-time Flight Timing Optimization of Airport Group Based on Weather Conditions

Abstract

During the execution of flight schedule, the capacity of airport and airspace is often reduced by external dynamic factors such as weather conditions and flow control, which makes it impossible to meet the flow demand of airport and airspace, resulting in flight delay. In order to better implement tactical management of air traffic flow and reduce flight delay time and delay cost, this paper considers the impact of weather conditions, and combines ground and air waiting strategies to construct a multi-objective short-term flight time optimization model based on weather conditions, and uses NSGA-II algorithm to solve it. Finally, the Yangtze River Delta Airport Group is taken as an example to verify. Introduction The planning and layout of regional airports has always been the core bottleneck of restricting the rapid development of regional air transport. With the single airport system becoming more and more difficult to meet the growing demand for air transport, multi-airport system (i.e. Airport group) with clear positioning and win-win cooperation in the region will inevitably become the future development trend. Because of the obvious air traffic interaction, limited airspace resources, strong demand for flight time and other reasons, airport groups are vulnerable to weather conditions, flow control and other external dynamic factors, resulting in lower than expected flight normal rate, largescale flight delay, which seriously affects the sustainable and healthy development of airport groups. Therefore, the implementation of scientific and reasonable optimization of short-term flight time is particularly important. At present, many researchers from all over the world have conducted research on airport group and flight time optimization issues. Rubin David (1976) began to study the airport group problem and first proposed the concept of an airport group, which briefly defined the airport group as "Multi Airport Region" [1]. Peter B (1994) analyzed the ground-holding policy of multiple airports in air traffic flow management and established a VBO model based on ground-holding policy [2]. Avijit Mukherjee (2007) established a dynamic random integer programming model based on weather forecast and ground-holding policy, and verified by example that the model can allocate flight time in different decision stages [3]. Husni Idris (2003) used the queuing model to analyze the collaborative operation of the New York airport group, focusing on the interaction of air traffic flows at airports within the airport group and the correlation of flight times at airports [4]. Alexandre Jacquillat (2013) used the delay value model and the Monte Carlo simulation model to approximate the dynamic characteristics of the airport queuing system, and analyzed the airport delay levels under different conditions, and optimized the flight time. [5,6]. Nikolas Pyrgiotis (2016) established a flight time optimization model considering the existing flight schedule and airline flight time requirements, and verified the model with New York Airport as an example [7]. Nuno Antunes (2018) established a multi-target flight time optimization model based on the flight time coordination mechanism and International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019) Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Intelligent Systems Research, volume 168