Affiliations: [a] Faculty of Electrical and Computer Engineering, University of Prishtina, Prishtina, Kosovo | [b] Faculty of Mathematical and Natural Sciences, University of Prishtina, Prishtina, Kosovo
Correspondence:
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Corresponding author: Kadri Sylejmani, Faculty of Electrical and Computer Engineering, University of Prishtina, Rr. “George Bush” p.n., Prishtina 10000, Kosovo. Tel.: +377 44 116 779; E-mail: kadri.sylejmani@uni-pr.edu.
Abstract: In typical cases, air traffic controllers make the schedule for arrivals and departures of aircraft on a runway based on the pre prepared schedule, where aircraft that are scheduled first are served first (a.k.a. First Come First Served – FCFS rule). In practice, it often happens that two or more aircraft are scheduled at the same time, and since only one can be served at a time, the others have to be shifted from service at a later time. Hence, the main issue stands at selecting the aircraft that have to be shifted, as, in general, their delay is correlated to excessive expense based on various factors, such as number of passengers, type of aircraft, precedence, ambient pollution, etc. For schedules with a large number of aircraft, deciding manually – which aircraft should be shifted based on FCFS sequence becomes quite complex. Consequently, in this paper, we present a genetic based algorithm to solve the problem of aircraft sequencing in a runway within a computation time of dozens of seconds by using a computing device with standard processing and memory capabilities. The results of the proposed algorithm are compared against three state of the art algorithms on existing test sets that in total consist of 527 instances. For 49.34% of instances, the proposed algorithm finds the optimal solutions, while its results are also quite competitive for difficult instances when compared against a state of the art solution based on the tabu search meta-heuristic. The computational results show that the proposed approach can be easily adapted and fine-tuned for application in practice.
Keywords: Air transport, aircraft sequencing, genetic algorithms
