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

Author

Listed:
  • Ming Zhang

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Qianwen Huang

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Sihan Liu

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Huiying Li

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

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.

Suggested Citation

  • Ming Zhang & Qianwen Huang & Sihan Liu & Huiying Li, 2019. "Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment," Sustainability, MDPI, vol. 11(23), pages 1-27, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6728-:d:291517
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    References listed on IDEAS

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    1. Julia Bennell & Mohammad Mesgarpour & Chris Potts, 2013. "Airport runway scheduling," Annals of Operations Research, Springer, vol. 204(1), pages 249-270, April.
    2. Adler, Nicole & Martini, Gianmaria & Volta, Nicola, 2013. "Measuring the environmental efficiency of the global aviation fleet," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 82-100.
    3. Zhou, Wenji & Wang, Tao & Yu, Yadong & Chen, Dingjiang & Zhu, Bing, 2016. "Scenario analysis of CO2 emissions from China’s civil aviation industry through 2030," Applied Energy, Elsevier, vol. 175(C), pages 100-108.
    4. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
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    Cited by:

    1. Jiang, Yu & Xue, Qingwen & Wang, Yasha & Cai, Mengting & Zhang, Honghai & Li, Yahui, 2021. "Traffic congestion mechanism in mega-airport surface," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 577(C).
    2. Wen Zhang & Qinghe Yuan & Shun Jia & Zhaojun (Steven) Li & Xianhui Yin, 2021. "Multi-Objective Optimization of Forth Flotation Process: An Application in Gold Ore," Sustainability, MDPI, vol. 13(15), pages 1-16, July.
    3. Hana Pačaiová & Peter Korba & Michal Hovanec & Jozef Galanda & Patrik Šváb & Ján Lukáč, 2021. "Use of Simulation Tools for Optimization of the Time Duration of Winter Maintenance Activities at Airports," Sustainability, MDPI, vol. 13(3), pages 1-14, January.

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