Mathematical programming formulations for robust airside terminal traffic flow optimisation problem

Kam K.H. Ng, Chun Hsien Chen, C. K.M. Lee

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)


The robust traffic flow modelling approach offers a perspicacious and holistic surveillance for flight activities in a nearby terminal manoeuvring area. The real time flight information expedites the streaming control of terminal operations using computational intelligence. Hence, in order to reduce the adverse effect of severe uncertainty and the impact of delay propagation, the amplified disruption along with the terminal traffic flow network can be leveraged by using robust optimisation. The transit time from entry waypoint to actual landing time is uncertain since the true airspeed is affected by the wind direction and hazardous aviation weather in the terminal manoeuvring area. Robust optimisation for TTFP is to generate a solution against the uncertain outcomes, which implies that less effort by the ATC to perform re-scheduling is required. In addition, two decomposition methods are presented and proposed in this work. The computational performance of traditional Benders Decomposition will largely be affected by the infeasibility in the subsystem and resolution of infeasible solution in the second-stage optimisation problem resulting in a long iterative process. Therefore, we presented an enhanced Benders Decomposition method to tackle the infeasibility in the subsystem. As shown in the numerical experiments, the proposed method outperforms the traditional Benders Decomposition algorithm using Wilcoxon-signed ranks test and achieved a 58.52% improvement of solution quality in terms of solving one-hour flight traffic scenarios with an hour computation time limit.

Original languageEnglish
Article number107119
JournalComputers and Industrial Engineering
Publication statusE-pub ahead of print - Apr 2021


  • Airside terminal traffic flow problem
  • Decomposition methods
  • Min–max approach
  • Robust optimisation

ASJC Scopus subject areas

  • General Computer Science
  • General Engineering


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