A real-time schedule method for aircraft landing scheduling problem based on cellular automaton

Yu Shenpeng*, Cao Xianbin, Hu Maobin, Du Wenbo, Zhang Jun

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

The Aircraft Landing Scheduling (ALS) problem is a typical hard multi-constraint optimization problem. In real applications, it is not most important to find the best solution but to provide a feasible landing schedule in an acceptable time. We propose a novel approach which can effectively solve the ALS while satisfying the real-time need. It consists of two steps: (i) Use CA to simulate the landing process in the terminal airspace and to find a considerably good landing sequence; (ii) a simple Genetic Algorithm associated with a Relaxation Operator is used to obtain a better result based on the CA result. Experiments have shown that our method is much faster and suitable for real-time ALS problem compared with traditional optimization methods. For all the 13 data sets, the proposed approach can find satisfactory solutions in less than 2 seconds.

Original languageEnglish
Title of host publication2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09
Pages717-723
Number of pages7
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09 - Shanghai, China
Duration: 12 Jun 200914 Jun 2009

Publication series

Name2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09

Conference

Conference2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09
Country/TerritoryChina
CityShanghai
Period12/06/0914/06/09

Keywords

  • Aircraft landing scheduling
  • Cellular automaton
  • Genetic algorithm
  • Optimization

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