Advertisement

Aircraft Landing Scheduling Based on Semantic Agent Negotiation Mechanism

  • Zhao-Tong Huang
  • Xue-Yan Song
  • Ji-Zhou Sun
  • Zhi-Zeng Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

The aircraft landing scheduling problem (ALS) is a typical NP-hard optimization problem and exists in the Air Traffic Control for a long time. Many algorithms have been proposed to solve the problem, and most of them are centralized. With the development of the aerotechnics, the concept of free flight has been proposed. Airplanes could change their flight paths during the flight without approval from a centralized en route control. In order to support free flight, a distributed system based on Multi-Agent System is proposed in this paper. The kernel of the system is semantic agent negotiation mechanism. With the method aircrafts in the system could make landing sequence considering their own states. The Experimental results show that the proposed algorithm is able to obtain an optimal landing sequence and landing time rapidly and effectively.

Keywords

Aircraft landing scheduling problem semantic agent negotiation free flight 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ciesielski, V., Scerri, P.: An Anytime Algorithm for Scheduling Aircraft Landing Times Using Genetic Algorithms. Australian Journal of Intelligent Information Processing System 4, 206–213 (1998)Google Scholar
  2. 2.
    Beasley, J.E., Krishnamoorthy, M., Sharaiha, Y.M., Abramson, D.: Scheduling Aircraft Landings - the Static Case. Transportation Science 34, 180–197 (2000)zbMATHCrossRefGoogle Scholar
  3. 3.
    Xu, X.H., Yao, Y.: Application of Genetic Algorithm to Aircraft Sequencing in Terminal Area. Journal of Traffic and Transportation Engineering 4, 121–126 (2004)Google Scholar
  4. 4.
    Yang, Q.: Scheduling Arrival Aircrafts on Multiple Runways Based on an Improved Genetic Algorithm. Journal of Sichuan University 38, 65–70 (2006)Google Scholar
  5. 5.
    Zhang, H., Hu, M.: Multi-runway Collaborative Scheduling Optimization of Aircraft Landing. Journal of Traffic and Transportation Engineering 9, 115–120 (2009)Google Scholar
  6. 6.
    Rong, J., Geng, S., Valasek, J., Ioerger, T.R.: Air Traffic Control Negotiation and Resolution Using an Onboard Multi-agent System. In: Proc. Digital Avionics Syst. Conf., vol. 2, pp. 7B2-1–7B2-12 (2002)Google Scholar
  7. 7.
    Pěchouček, M., Šišlák, D.: Agent-based Approach to Free-flight Planning, Control, and Simulation. IEEE Intell. Syst. 24(1), 14–17 (2009)CrossRefGoogle Scholar
  8. 8.
    Lomuscio, R., Wooldridge, M., Jennings, N.R.: A Classification Scheme for Negotiation in Electronic Commerce. Int. Journal of Group Decision and Negotiation 12(1), 31–56 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhao-Tong Huang
    • 1
  • Xue-Yan Song
    • 1
  • Ji-Zhou Sun
    • 1
  • Zhi-Zeng Li
    • 1
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina

Personalised recommendations