Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment

Abstract

High-efficiency taxiing for safe operations is needed by all types of aircraft in busy airports to reduce congestion and lessen fuel consumption and carbon emissions. This task is a challenge in the operation and control of the airport’s surface. Previous studies on the optimization of aircraft taxiing on airport surfaces have rarely integrated waiting constraints on the taxiway into the multi-objective optimization of taxiing time and fuel emissions. Such studies also rarely combine changes to the airport’s environment (such as airport elevation, field pressure, temperature, etc.) with the multi-objective optimization of aircraft surface taxiing. In this study, a multi-objective optimization method for aircraft taxiing on an airport surface based on the airport’s environment and traffic conflicts is proposed. This study aims to achieve a Pareto optimized taxiing scheme in terms of taxiing time, fuel consumption, and pollutant emissions. This research has the following contents: (1) Previous calculations of aircraft taxiing pathways on the airport’s surface have been based on unimpeded aircraft taxiing. Waiting on the taxiway is excluded from the multi-objective optimization of taxiing time and fuel emissions. In this study, the waiting points were selected, and the speed curve was optimized. A multi-objective optimization scheme under aircraft taxiing obstacles was thus established. (2) On this basis, the fuel flow of different aircraft engines was modified with consideration to the aforementioned environmental airport differences, and a multi-objective optimization scheme for aircraft taxiing under different operating environments was also established. (3) A multi-objective optimization of the taxiing time and fuel consumption of different aircraft types was realized by acquiring their parameters and fuel consumption indexes. A case study based on the Shanghai Pudong International Airport was also performed in the present study. The taxiway from the 35R runway to the 551# stand in the Shanghai Pudong International Airport was optimized by the non-dominant sorting genetic algorithm II (NSGA-II). The taxiing time, fuel consumption, and pollutant emissions at this airport were compared with those of the Kunming Changshui International Airport and Lhasa Gonggar International Airport, which have different airport environments. Our research conclusions will provide the operations and control departments of airports a reference to determine optimal taxiing schemes.

Multi-Objective Optimization of Forth Flotation Process: An Application in Gold Ore

“…The implementation process of the NSGA-II algorithm is shown in Figure 4. The adoption of crowding degree and crowding comparison operators not only overcomes the disadvantage of sharing parameters that need to be artificially specified in the NSGA, but also ensures the diversity of the population [23]. The NSGA-II algorithm can be carried out according to the following steps: 1.…”

Multi-Objective Route Planning for Aircraft Taxiing Under Different Traffic Conflict Types

“…Therefore, the fuel flows and pollutant emission indices must be corrected. Doug and Paynter [23] and Zhang et al [59] reported that the sea level fuel flow of a single engine from aircraft type i when it is at the taxiing state j under the international standard atmospheric (ISA) condition is f ij0 . The correction formulas to convert f ij0 into the practical fuel flow f ij under practical field pressure P and air temperature T at airports are follows: Given that this study focuses on aircraft taxiing, and the taxiing speed is far smaller than the sound velocity, the Mach number M is approximately zero.…”

“…The airport environmental changes can influence the fuel consumption for surfacing taxiing of aircraft. Considering the differences of field pressure and air temperature [59], the fuel flow coefficient of aircraft was corrected through the method in Sec. IV.A.…”

A Priority-Based Conflict Resolution Strategy for Airport Surface Traffic Considering Suboptimal Alternative Paths

“…Evertse and Visser [10] designed a realtime airport surface movement planning tool, which was capable of creating conflict-free timed taxiing trajectories for aircraft, while minimizing the total emission of pollutants, fuel usage, departure slot time deviations and taxi time. Zhang et al [11] proposed a multi-objective optimization method for aircraft taxiing based on the airport's environment and traffic conflicts. With the in-depth study of taxiway optimization, in order to simulate the operation of airports, more and more detailed factors were incorporated into aircraft taxiing models.…”