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Hybrid tabu searches for effective airport gate management

Author

Listed:
  • Chun-Hung Cheng
  • Angappa Gunasekaran
  • Sin C. Ho
  • Chuek-Lam Kwan
  • Tobun Dorbin Ng

Abstract

In this research, we are concerned with assigning gates of an airport to arriving and departing aircrafts. This is referred to as the gate assignment problem (GAP). This is an important planning problem, as improper assignment may result in flight delays and inefficient use of airport resources. As solving this problem to optimality is ineffective for many realistic situations, we examine the use of a meta-heuristic. Specifically, we attempt to use tabu search (TS). Although the application of TS in GAP is not new, we explore to introduce path relinking (PR) to improve the performance of TS. In our computation, we find that the PR feature produces desirable results. Further, the experiment using flight data from Incheon International Airport of Korea (ICN) shows that TS+PR performs well when compared with meta-heuristics such as genetic search (GS), simulated annealing (SA), a pure tabu search (TS), and a hybrid of SA and TS.

Suggested Citation

  • Chun-Hung Cheng & Angappa Gunasekaran & Sin C. Ho & Chuek-Lam Kwan & Tobun Dorbin Ng, 2017. "Hybrid tabu searches for effective airport gate management," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 30(4), pages 484-522.
    • Handle: RePEc:ids:ijores:v:30:y:2017:i:4:p:484-522
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    Cited by:

    1. Li, Mingjie & Hao, Jin-Kao & Wu, Qinghua, 2022. "Learning-driven feasible and infeasible tabu search for airport gate assignment," European Journal of Operational Research, Elsevier, vol. 302(1), pages 172-186.

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