Agent-based modelling and simulation for life-cycle airport flight planning and scheduling

ABSTRACT

The airport flight planning and scheduling (AFPS) process involves many sub-systems in airport management, including flight arrival prediction, flight landing prioritisation, flight route scheduling, flight departure prioritisation and airport big-data planning. Existing literature has mainly focused on advancing the efficiency of specific sub-system(s) of AFPS. Therefore, there is a call to investigate the AFPS comprehensively and improve its performance systematically, instead of solely enhancing partial sub-system(s) in an airport. This paper proposes a life cycle analysis (LCA) framework to describe the “life cycle” that how an aircraft interacts with the AFPS system of an airport. The agent-based modelling and simulation technique is used to simulate the AFPS system under the LCA framework by treating each aircraft as a passive agent following the orders from the AFPS system. We demonstrate that the naive first-in-first-out principle can be modified to some simple prioritising rules to significantly reduce the overall delay (including importance-weighted delay) and processing time of flights, as well as to increase the number of departed aircraft. Hence, the produced agent-based model allows us to improve AFPS performance by optimising multiple sub-systems simultaneously.

KEYWORDS:

• Simulation
• agent-based modelling
• airport flight
• planning
• scheduling

Acknowledgements

The authors greatly appreciate the editor and anonymous referees for their valuable comments, which greatly helped to improve this paper. This research was supported by the National Natural Science Foundation of China [grant numbers 71901202, 72271227], the Outstanding Talent Supporting Plan of BFSU, the Fundamental Research Funds for the Central Universities, the Youth Innovation Promotion Association CAS, the MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS, and the Weiqiao Guoke Joint Laboratory at UCAS.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. We thank an anonymous referee for providing this insightful comment.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 71901202, 72271227], the Outstanding Talent Supporting Plan of BFSU, the Fundamental Research Funds for the Central Universities, the Youth Innovation Promotion Association CAS, the MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS, and the Weiqiao Guoke Joint Laboratory at UCAS.