Chapter 5. Airport Traffic Management
5.2. Gate assignment
Gate assignment appears to be a first important step in the daily airport planning process: it consists of deciding which gate will be used by which aircraft at which
time. It can be considered in advance for some given fixed flights (static approach) but is also a real-time problem for airlines and airport services, when some changes have to be processed in the initial gate assignment schedule due to unexpected events (gate reassignment problem).
In any case, it involves a lot of operational and complex constraints, which can be different from one airport to another. Each gate is only compatible with a subset of aircraft types, and in some airports, some groups of gates can be configured differently to accept more or less aircraft of different sizes at the same time. Each gate also offers different facilities (remote from or fixed to a terminal, in or out of the international zone, with or without fueling or deicing services, etc.). These properties can be modeled as constraints or preferences for airlines. More generally, various objectives can be considered in the gate assignment problem, depending on the considered recipients:
– minimizing the passengers’ transport times and walking distances (at the departure, during transit and at the arrival);
– minimizing the airline operating costs and the deviations to airline preferences (including baggage or freight transport, aircraft towing operations, gate facilities, fleet assignment, crew scheduling, etc.);
– minimizing the taxi times of aircraft, including the delay due to aircraft waiting for the availability of their gate, and minimizing the risk of such situations;
– maximizing the robustness of the solution, in case of deviations to the initial schedule, due to flight delay or equipment failures.
Thus, the gate assignment problem can be tackled with more or less realism. It can be considered as a mono-objective problem (by weighting together the different criteria) or as a multiobjective problem (providing multiple equivalent solutions to the airport operations). In the literature, the problem is most of the time expressed in the binary or integer programming formalism and some detailed comparative surveys can be found in [DOR 07] and [BOU 14].
5.2.2. Resolution methods
Some attempts to solve the gate assignment problem on actual airport flight data with exact methods (yielding and proving an optimal solution) can be found in the literature, using, for example, linear programming relaxation at Toronto International Airport [MAN 85] or branch and bound techniques at King Khalid International Airport [BOL 00] and at Chiang Kai-Shek Airport in a multiobjective formulation [YAN 01]. These kinds of attempts are promising but remain hardly applicable alone in a real-time environment because of their necessary computation time on large instances or because of their lack of practical relevance.
Flight 5 Flight 2 Flight 1
Gate 1 Flight 3
Flight 4
Gate 2 Flight 5
Insert move : change the gate of a flight
Flight 2 Flight 1
Gate 1 Flight 3
Flight 4
Gate 2 Flight 6
Exchange move : swap the gates of groups of flights
Figure 5.2.Neighborhood moves for the gate assignment problem
For these reasons, the gate assignment problem is also often solved using local methods (that do not ensure an optimal solution can be found). In [MAN 85] at Toronto International Airport, the authors observed that a heuristic method can find in a few seconds some near-optimal solutions (with only3:9%higher walking distances per passengers, compared to the best solution found by their exact method). They also measured that the solutions found by the heuristic method can be used as an initial solution for their exact method, in order to significantly reduce its CPU time.
Metaheuristics are largely applied to the problem, as they can provide quickly some good solutions, and are easily adaptable to new constraints or new criteria, while maintaining a theoretical chance of finding the optimal solution.
In [GU 99], the authors tested the efficiency of a genetic algorithm to minimize the extra delay in the problem of gate reassignment, when the scheduled gate of an arriving aircraft is still occupied by a delayed departure. They obtained some solutions that appear at least as effective as those found by experienced gate managers.
Considering the static aircraft-gate assignment problem, with the objective of minimizing the dispersion of idle time periods [BOL 01], genetic algorithms are also used to find good alternative solutions to the single one provided by an exact method based on mathematical models. In [XU 01], the authors solved a more dynamic gate assignment problem with a tabu search, taking advantage of the special properties of different types of neighborhood moves (see Figure 5.2).
In [HU 07], the authors considered the gate assignment problem, with the objective of minimizing a balance between the aircraft waiting times on the aprons, the passenger walking distances and the baggage transport distances. They compared
different possible encoding to solve the problem with a genetic algorithm, and they found out that a binary encoding, combined with a uniform crossover, makes the genetic algorithm more efficient.
Hybridization between different metaheuristics is also often used in the literature to improve the efficiency of the algorithms. In [CHE 12], on the same instances of the gate assignment problem at Incheon International Airport, a hybridization between a simulated annealing algorithm and a tabu search yielded better results than those obtained with the two metaheuristics alone.
When there are not enough gates for all the scheduled flights, the gate assignment problem is said to be over-constrained: in this case, a new objective is to minimize the number of flights that cannot be assigned a gate. In [DIN 05], the authors solved this problem with a new hybridization between a simulated annealing algorithm and a tabu search. They used the same kind of neighborhood operators as those described in [XU 01] to facilitate the use of heuristics and improve their results.
When a departure metering strategy is used at an airport (with a DMAN system), some departing aircraft are held at gate, which can create gate conflicts with some arriving aircraft. The initial gate assignment can try to minimize the probability of these conflicts, by maximizing the time gap between two consecutive flights at the same gate: in [KIM 14], using some actual flight data of New York-LaGuardia Airport, the authors used a tabu search with two neighborhood operators, in order to build a robust gate assignment, and measured a significant diminution of gate conflicts in their simulations.
5.3. Runway scheduling