Latest revision as of 11:43, 22 January 2021

Abstract

We consider the assignment of gates to arriving and departing flights at a large hub airport. This problem is highly complex even in planning stage when all flight arrivals and departures are assumed to be known precisely in advance. There are various considerations that are involved while assigning gates to incoming and outgoing flights (such a flight pair for the same aircraft is called a turn) at an airport. Different gates have restrictions, such as adjacency, last-in first-out gates and towing requirements, which are known from the structure and layout of the airport. Some of the cost components in the objective function of the basic assignment model include notional penalty for not being able to assign a gate to an aircraft, penalty for the cost of towing an aircraft with a long layover, and penalty for not assigning preferred gates to certain turns. One of the major contributions of this paper is to provide mathematical model for all these complex constraints that are observed at a real airport. Further, we study the problem in both planning and operations modes simultaneously, and such an attempt is, perhaps, unique and unprecedented. For planning mode, we sequentially introduce new additional objectives to our gate assignment problem that have not been studied in the literature so far(i) maximization of passenger connection revenues, (ii) minimization of zone usage costs, and (iii) maximization of gate plan robustnessand include them to the model along with the relevant constraints. For operations mode, the main objectives studied in this paper are recovery of schedule by minimizing schedule variations and maintaining feasibility by minimal retiming in the event of major disruptions. Additionally, the operations mode models must have very, very short run times of the order of a few seconds. These models are then applied to a functional airline at one of its most congested hubs. Implementation is carried out using Optimization Programming Language, and computational results for actual data sets are reported. For the planning mode, analyst perception of weights for the different objectives in the multi-objective model is used wherever actual dollar value of the objective coefficient is not available. The results are also reported for large, reasonable changes in objective function coefficients. For the operations mode, flight delays are simulated, and the performance of the model is studied. The final results indicate that it is possible to apply this model to even large real-life problems instances to optimality within short run times with clever formulation of conventional continuous time assignment model. Copyright (c) 2013 John Wiley & Sons, Ltd.

Original document

The different versions of the original document can be found in:

• http://infoscience.epfl.ch/record/195862

• https://infoscience.epfl.ch/record/196071/files/KumarBier12.pdf

• https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fatr.1235,

http://dx.doi.org/10.1002/atr.1235 under the license http://doi.wiley.com/10.1002/tdm_license_1.1

• https://infoscience.epfl.ch/record/196071/files/KumarBier12.pdf,

http://infoscience.epfl.ch/record/196071

• https://onlinelibrary.wiley.com/doi/full/10.1002/atr.1235,

https://transp-or.epfl.ch/documents/technicalReports/KumarBier12.pdf,

https://onlinelibrary.wiley.com/doi/pdf/10.1002/atr.1235,

https://infoscience.epfl.ch/record/195862,

https://www.scipedia.com/public/Kumar_Bierlaire_2014a,

http://transp-or.epfl.ch/documents/technicalReports/KumarBier12.pdf,

http://doi.wiley.com/10.1002/atr.1235,

https://infoscience.epfl.ch/record/196071/files/KumarBier12.pdf,

https://academic.microsoft.com/#/detail/1894418030