Abstract
Regarding the large number of flights that a hub airport usually has to serve and the competitiveness in the aviation industry, optimal scheduling of limited and expensive airport resources such as gates is really vital. This work focuses on the efficient scheduling of airport gates to achieve a balance between three important goals, namely reducing the walking distance of passengers, decreasing the number of flights assigned to the gates different from their reference gates as well as widening the total shopping area passed by passengers while walking to, from or between the gates. A set of different scenarios is considered for the arrival of flights regarding the possible delays. Robust multi-objective optimisation is followed through an exact solution approach according to the weighted sum method by the Baron solver as well as a metaheuristic method consisting of the hybridisation of multi-objective particle swarm optimisation (MOPSO) and the multi-objective simulated annealing (MOSA). The sets of Pareto-optimal solutions obtained by these two methods along with those of the pure MOPSO, MOSA and a tabu search algorithm from the literature are compared based on some evaluation metrics and with the aid of a statistical test.
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References
GAMS Development Corporation, General Algebraic Modeling System (GAMS) Release 24.2.1. Washington, DC, USA (2013)
Aktel, A., Yagmahan, B., Özcan, T., Yenisey, M.M., Sansarcı, E.: The comparison of the metaheuristic algorithms performances on airport gate assignment problem. Transp. Res. Procedia 22, 469–478 (2017). https://doi.org/10.1016/j.trpro.2017.03.061
Ben-Tal, A., Nemirovski, A.: Robust solutions of uncertain linear programs. Oper. Res. Lett. 25(1), 1–13 (1999). https://doi.org/10.1016/s0167-6377(99)00016-4
Bergmann, B., Hommel, G.: Improvements of general multiple test procedures for redundant systems of hypotheses. In: Multiple Hypothesenprüfung / Multiple Hypotheses Testing, pp. 100–115. Springer, Heidelberg (1988). https://doi.org/10.1007/978-3-642-52307-6_8
Cheng, C.H., Ho, S.C., Kwan, C.L.: The use of meta-heuristics for airport gate assignment. Expert Syst. Appl. 39(16), 12430–12437 (2012). https://doi.org/10.1016/j.eswa.2012.04.071
Daş, G.S.: New multi objective models for the gate assignment problem. Comput. Ind. Eng 109, 347–356 (2017). https://doi.org/10.1016/j.cie.2017.04.042
Daş, G.S., Gzara, F., Stützle, T.: A review on airport gate assignment problems: single versus multi objective approaches. Omega 92, 102146 (2020). https://doi.org/10.1016/j.omega.2019.102146
Dell’Orco, M., Marinelli, M., Altieri, M.G.: Solving the gate assignment problem through the fuzzy bee colony optimization. Transp. Res. Part C Emerg. Technol 80, 424–438 (2017). https://doi.org/10.1016/j.trc.2017.03.019
Deng, W., Zhao, H., Yang, X., Xiong, J., Sun, M., Li, B.: Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. Appl. Soft Comput. 59, 288–302 (2017). https://doi.org/10.1016/j.asoc.2017.06.004
Dijk, B., Santos, B.F., Pita, J.P.: The recoverable robust stand allocation problem: a GRU airport case study. OR Spectr. 41(3), 615–639 (2018). https://doi.org/10.1007/s00291-018-0525-3
Dorndorf, U., Jaehn, F., Pesch, E.: Flight gate assignment and recovery strategies with stochastic arrival and departure times. OR Spectr. 39(1), 65–93 (2016). https://doi.org/10.1007/s00291-016-0443-1
Freathy, P., O’Connell, F.: Planning for profit: the commercialization of European airports. Long Range Plan. 32(6), 587–597 (1999). https://doi.org/10.1016/s0024-6301(99)00075-8
Genç, H.M., Erol, O.K., Eksin, İ, Berber, M.F., Güleryüz, B.O.: A stochastic neighborhood search approach for airport gate assignment problem. Expert Syst. Appl. 39(1), 316–327 (2012). https://doi.org/10.1016/j.eswa.2011.07.021
Geuens, M., Vantomme, D., Brengman, M.: Developing a typology of airport shoppers. Tour. Manag. 25(5), 615–622 (2004). https://doi.org/10.1016/j.tourman.2003.07.003
Kaliszewski, I., Miroforidis, J., Stańczak, J.: The airport gate assignment problem multi-objective optimization versus evolutionary multi-objective optimization. Comput. Sci. 18(1), 41–52 (2017). https://doi.org/10.7494/csci.2017.18.1.41
Kumar, V.P., Bierlaire, M.: Multi-objective airport gate assignment problem in planning and operations. J. Adv. Transp 48(7), 902–926 (2013). https://doi.org/10.1002/atr.1235
Mokhtarimousavi, S., Talebi, D., Asgari, H.: A non-dominated sorting genetic algorithm approach for optimization of multi-objective airport gate assignment problem. Transp. Res. Rec. J. Transp. Res. Board 2672(23), 59–70 (2018). https://doi.org/10.1177/0361198118781386
Montgomery, D.C.: Design and Analysis of Experiments. John Wiley & Sons, Inc., Hoboken (2006)
Nikulin, Y., Drexl, A.: Theoretical aspects of multicriteria flight gate scheduling: deterministic and fuzzy models. J. Schedul. 13(3), 261–280 (2009). https://doi.org/10.1007/s10951-009-0112-1
Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method in multiobjective problems. In: Proceedings of the 2002 ACM symposium on Applied computing (SAC). ACM Press (2002). https://doi.org/10.1145/508791.508907
Pternea, M., Haghani, A.: Mathematical models for flight-to-gate reassignment with passenger flows: state-of-the-art comparative analysis, formulation improvement, and a new multidimensional assignment model. Comput. Ind. Eng. 123, 103–118 (2018). https://doi.org/10.1016/j.cie.2018.05.038
Richter, S., Voss, S., Wulf, J.: A passenger movement forecast and optimisation system for airport terminals. Int. J. Aviat. Manag. 1(1/2), 58 (2011). https://doi.org/10.1504/ijam.2011.038293
Riquelme, N., Von Lücken, C., Baran, B.: Performance metrics in multi-objective optimization. In: 2015 Latin American Computing Conference (CLEI), pp. 1–11 (2015). https://doi.org/10.1109/CLEI.2015.7360024
van Schaijk, O.R.P., Visser, H.G.: Robust flight-to-gate assignment using flight presence probabilities. Transp. Plan. Technol 40(8), 928–945 (2017). https://doi.org/10.1080/03081060.2017.1355887
Serafini, P.: Simulated annealing for multi objective optimization problems. In: Multiple Criteria Decision Making, pp. 283–292. Springer, New York (1994). https://doi.org/10.1007/978-1-4612-2666-6_29
Yan, S., Tang, C.H.: A heuristic approach for airport gate assignments for stochastic flight delays. Eur. J. Oper. Res. 180(2), 547–567 (2007). https://doi.org/10.1016/j.ejor.2006.05.002
Yu, C., Zhang, D., Lau, H.Y.: An adaptive large neighborhood search heuristic for solving a robust gate assignment problem. Expert Syst. Appl. 84, 143–154 (2017). https://doi.org/10.1016/j.eswa.2017.04.050
Yu, C., Zhang, D., Lau, H.: MIP-based heuristics for solving robust gate assignment problems. Comput. Ind. Eng. 93, 171–191 (2016). https://doi.org/10.1016/j.cie.2015.12.013
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Nourmohammadzadeh, A., Voß, S. (2021). Robust Multi-Objective Gate Scheduling at Hub Airports Considering Flight Delays: A Hybrid Metaheuristic Approach. In: Mes, M., Lalla-Ruiz, E., Voß, S. (eds) Computational Logistics. ICCL 2021. Lecture Notes in Computer Science(), vol 13004. Springer, Cham. https://doi.org/10.1007/978-3-030-87672-2_39
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