The Airport Gate Assignment Problem: A Survey

Table 1

Formulations of AGAP and related problems.

Formulation	References	Criterion (comments)	Problem type
Integer linear programming (IP)	Lim et al. [24]	(i) Minimizing the sum of the delay penalties; (ii) Minimizing the total walking distance	Theoretical
	Diepen et al. [25]	(i) Minimizing the deviation of arrival and departure time; (ii) Minimizing replanning the schedule	Real case (Amsterdam Airport Schiphol)
	Diepen et al. [26]	Minimizing the deviations from the expected arrival and departure times	Real case (Amsterdam Airport Schiphol)
Binary integer programming	Mangoubi and Mathaisel [11]; Yan et al. [29]	Minimizing passenger walking distances	Real case (Toronto International Airport); Real case (Chiang Kai-Shek Airport)
	Vanderstraeten and Bergeron [28]	Minimizing the number off-gate event	Theoretical
	Bihr [12]	Minimizing of the total passenger distance	Theoretical
	Tang et al. [27]	Developing a gate reassignment framework and a systematic computerized tool	Real case (Taiwan International Airport)
	Prem Kumar and Bierlaire [18]	(i) Maximizing the gate rest time between two turns; (ii) Minimizing the cost of towing an aircraft with a long turn; (iii) Minimizing overall costs that include penalization for not assigning preferred gates to certain turns	Theoretical
Mixed integer linear programming (MILP)	Bolat [30]	Minimizing the range of slack times	Real case (King Khaled International Airport)
	Bolat [31]	Minimizing the variance or the range of gate idle time	Real case (King Khaled International Airport)
Mixed integer nonlinear programming	Li [5, 32]	Minimizing the number of gate conflicts of any two adjacent aircrafts assigned to the same gate	Real case (Continental Airlines, Houston Gorge Bush Intercontinental Airport)
	Bolat [31]	Minimizing the variance or the range of gate idle time	Real case (King Khaled International Airport)
Multiple objective GAP formulations	Hu and Di Paolo [36]	Minimize passenger walking distance, baggage transport distance, and aircraft waiting time on the apron	Theoretical
	Wei and Liu [16]	(i) Minimizing the total walking distance for passengers; (ii) Minimizing the variance of gates idle times	Theoretical
	B.A.C.o.E.B. Team and A.I.C.o.E. Team [17]	(i) Minimizing walking distance; (ii) Maximizing the number of gated flights; (iii) Minimizing flight delays	Theoretical
	Yan and Huo [2]	(i) Minimizing passenger walking distances; (ii) Minimizing the passenger waiting time	Real case (Chiang Kai-Shek Airport)
	Kaliszewski and Miroforidis [37]	Finding gate assignment efficiency which represents rational compromises between waiting time for gate and apron operations	Theoretical
Stochastic model	Yan and Tang [10]	Minimizing the total passenger waiting time	Real case (Taiwan International Airport)
	Genç et al. [38]	Maximizing gate duration, which is total time of the gates allocated	Theoretical and real case (Ataturk Airport of Istanbul, Turkey)
	Şeker and Noyan [9]	Minimizing the expected variance of the idle time	Theoretical
Quadratic assignment problem (QAP)	Drexl and Nikulin [3]	(i) Minimizing the number of ungated flights; (ii) Minimizing the total passenger walking distances or connection times; (iii) Maximizing the total gate assignment preferences	Theoretical
	Haghani and Chen [13]	Minimizing the total passenger walking distances	Theoretical
Scheduling problems	Li [39]	(i) Maximizing the sum of the all products of the flight eigenvalue; (ii) Maximizing the gate eigenvalue that the flight assigned	Theoretical
Quadratic mixed binary programming	Bolat [34]	Minimizing the variance of idle times	Real case (King Khaled International Airport)
	Zheng et al. [33]	Minimizing the overall variance of slack time	Real case (Beijing International Airport, China)
	Xu and Bailey [14]	Minimizing the passenger connection time	Theoretical
Binary quadratic programming	Ding et al. [6, 7, 35]	Minimize the number of ungated flights and the total walking distances or connection times	Theoretical
Clique partitioning problem (CPP)	Dorndorf et al. [8]	(i) Maximizing the total assignment preference score; (ii) Minimizing the number of unassigned flights; (iii) Minimizing the number of tows; (iv) Maximizing the robustness of the resulting schedule	Theoretical
Network representation	Maharjan and Matis [40]	Minimizing both fuel burn of aircraft and the comfort of connecting passengers	Real case (Continental Airlines at George W. Bush Intercontinental Airport in Houston (IAH))
Robust optimization	Diepen et al. [41]	Maximizing the robustness of a solution to the gate assignment problem	Real case (Amsterdam Airport Schiphol)