assignment problem for gate

Whale Optimization Algorithm for Airport Gate Assignment Problem

Conference paper
First Online: 02 March 2023
Cite this conference paper

Mert Paldrak 12 &
Mustafa Arslan Örnek 12

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Included in the following conference series:

The International Symposium for Production Research

856 Accesses

1 Citations

In view of the rapid increase in the volume of air traffic, optimization of airport management has recently gained a great deal of attention to be able to increase the airport capacity and efficiently use scarce resources, namely gates. Improper assignment of gates causes flight delays, inefficient usage of scarce resources, customers’ dissatisfaction and other domino effects. Generally, a typical hub-and-spoke handles hundreds of flights every day. Considering this, the gate assignment problem (GAP) addresses the issue of maximizing the usage of gates equipped with aerobridges, namely bridge-equipped gates. Due to the numerous flights and gates involved in the problem, it is often impractical to solve GAP with optimality in a reasonable amount of computational time. Consequently, novel nature-inspired heuristics have been proposed to generate good solutions to AGAP. In this study, we employ Whale Optimization Algorithm (WOA) which is one of the recently developed swarm-based metaheuristics to find good solutions to complex GAP. The proposed method assigns scheduled flights to bridge-equipped gates based on both total flight-to-gate assignment utility and use of apron gates. In order to demonstrate the efficiency of the algorithm, some instances with different sizes are generated and the results obtained by using CPLEX Studio IDE optimizer and WOA are compared with respect to solution quality and computational time. To ameliorate the solution quality, we proposed two local search procedures embedded in WOA. To the best of our knowledge, WOA has never been applied to GAP so far. Thus, the chief contribution of this study is to apply such novel swarm-based metaheuristic, namely WOA to GAP. Comparison of the results with the optimal schedules has allowed us to demonstrate the power of the proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Available as PDF
Read on any device
Instant download
Own it forever
Available as EPUB and PDF
Compact, lightweight edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info
Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Ballis, A.: Simulation of airport terminal facilities in the Greek airports of Kavala and Alexandroupolis. Oper. Res. Int. Journal 2 (3), 391–406 (2002)

Article Google Scholar

Ding, H., et al.: The over-constrained airport gate assignment problem. Comput. Oper. Res. 32 (7), 1867–1880 (2005)

Google Scholar

Ornek, M.A., Ozturk, C., Sugut, I.: Integer and constraint programming model formulations for flight-gate assignment problem. Oper. Res., 1–29 (2020)

Obata, T.: The Quadratic Assignment Problem: Evaluation of Exact and Heuristic Algorithms. Rensselaer Polytechnic Institute, New York (1979)

Brazile, R.P., Swigger, K.M.: GATES: an airline gate assignment and tracking expert system. IEEE Intell. Syst. 3 (02), 33–39 (1988)

Gosling, G.D.: Design of an expert system for aircraft gate assignment. Transp. Res. Part A General 24 (1), 59–69 (1990)

Su, Y.Y., Srihari, K.: A knowledge-based aircraft-gate assignment advisor. Comput. Ind. Eng. 25 (1–4), 123–126 (1993)

Cheng, C.-H., Ho, S.C., Kwan, C.-L.: The use of meta-heuristics for airport gate assignment. Exp. Syst. Appl. 39 (16), 12430–12437 (2012)

Babić, O., Teodorović, D., Tošić, V.: Aircraft stand assignment to minimize walking. J. Transp. Eng. 110 (1), 55–66 (1984)

Bolat, A.: Procedures for providing robust gate assignments for arriving aircrafts. Eur. J. Oper. Res. 120 (1), 63–80 (2000)

Article MATH Google Scholar

Bolat, A.: Models and a genetic algorithm for static aircraft-gate assignment problem. J. Oper. Res. Soc. 52 (10), 1107–1120 (2001)

Yan, S., Huo, C.-M.: Optimization of multiple objective gate assignments. Transp. Res. Part A Policy Pract. 35 (5), 413–432 (2001)

Mangoubi, R.S., Mathaisel, D.F.X.: Optimizing gate assignments at airport terminals. Transp. Sci. 19 (2), 173–188 (1985)

Sherali, H.D.: A quadratic partial assignment and packing model and algorithm for the airline gate assignment problem. Quadratic Assignment Relat. Probl. 16 , 343–364 (1994)

Karsu, Ö., Azizoğlu, M., Alanlı, K.: Exact and heuristic solution approaches for the airport gate assignment problem. Omega 103 , 102422 (2021)

Haghani, A., Chen, M.-C.: Optimizing gate assignments at airport terminals. Transp. Res. Part A Policy Pract. 32 (6), 437–454 (1998)

Gu, Y., Chung, C.A.: Genetic algorithm approach to aircraft gate reassignment problem. J. Transp. Eng. 125 (5), 384–389 (1999)

Xu, J., Bailey, G.: The airport gate assignment problem: mathematical model and a TABU search algorithm. In: Proceedings of the 34th Annual Hawaii International Conference on System Sciences. IEEE (2001)

Lim, A., Wang, F.: Robust airport gate assignment. In: 17th IEEE International Conference Tools with Artificial Intelligence, ICTAI 05, pp. 74–81 (2005)

Dell'Orco, M., Marinelli, M., Altieri, M.G.: Solving the gate assignment problem through the fuzzy bee colony optimization. Transp. Res. Part C Emerging Technol. 80 , 424–438 (2017)

Ding, J., Zhang, Y.: A discrete particle swarm optimization algorithm for gate and runway combinatorial optimization problem. Res. J. Appl. Sci. Eng. Technol. 5 (10), 2997–3003 (2013)

Zhao, Hui, and Liqin Cheng. “Ant colony algorithm and simulation for robust airport gate assignment.“ Mathematical Problems in Engineering 2014 (2014)

Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95 , 51–67 (2016)

Drexl, A., Nikulin, Y.: Multicriteria airport gate assignment and Pareto simulated annealing. IIE Trans. 40 (4), 385–397 (2008)

Paldrak, M., Örnek, M.A.: A GRASP algorithm for multi-objective airport gate assignment problem. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds.) International Conference on Intelligent and Fuzzy Systems. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09176-6_63

Download references

Author information

Authors and affiliations.

Industrial Engineering, Yasar University, Bornova, Türkiye

Mert Paldrak & Mustafa Arslan Örnek

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mert Paldrak .

Editor information

Editors and affiliations.

Institute of Production Engineering and Photonic Technologies, TU Wien, Vienna, Wien, Austria

Numan M. Durakbasa

Faculty of Engineering and Natural Sciences, Industrial Engineering Department, Istanbul Zaim University, Istanbul, Türkiye

M. Güneş Gençyılmaz

Rights and permissions

Reprints and permissions

Copyright information

About this paper

Cite this paper.

Paldrak, M., Örnek, M.A. (2023). Whale Optimization Algorithm for Airport Gate Assignment Problem. In: Durakbasa, N.M., Gençyılmaz, M.G. (eds) Towards Industry 5.0. ISPR 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-24457-5_39

Download citation

DOI : https://doi.org/10.1007/978-3-031-24457-5_39

Published : 02 March 2023

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-24456-8

Online ISBN : 978-3-031-24457-5

eBook Packages : Engineering Engineering (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

We're Hiring!
Help Center

Airport Gate Assignment problem: Mathematical formulation and resolution

International Conference on Reasoning and Optimisation in Information Systems (ROIS2013)

Related Papers

Ahmed Alsultan

TheScientificWorldJournal

Ahmed salem

The airport gate assignment problem (AGAP) is one of the most important problems operations managers face daily. Many researches have been done to solve this problem and tackle its complexity. The objective of the task is assigning each flight (aircraft) to an available gate while maximizing both conveniences to passengers and the operational efficiency of airport. This objective requires a solution that provides the ability to change and update the gate assignment data on a real time basis. In this paper, we survey the state of the art of these problems and the various methods to obtain the solution. Our survey covers both theoretical and real AGAP with the description of mathematical formulations and resolution methods such as exact algorithms, heuristic algorithms, and metaheuristic algorithms. We also provide a research trend that can inspire researchers about new problems in this area.

Transportation Research Part E: Logistics and Transportation Review

Nilay Noyan

Operational Research

Arslan M Ornek

In this paper, we consider the over-constrained airport gate assignment problem where the number of flights exceeds the number of gates available, and where the objectives are to minimize the number of ungated flights and the total walking distances or connection. We design a greedy algorithm and use a tabu search meta-heuristic to solve the problem. The greedy algorithm minimizes ungated flights while providing initial feasible solutions while we devise a new neighborhood search technique, the interval exchange move, which allows us flexibility in seeking good solutions, especially in case when flight schedules are dense in time. Experiment conducted give good results.

Computers & Operations Research

Expert Systems with Applications

Iie Transactions

Yury Nikulin

This article has been retracted.

Managing the gate assignment problem in the hub airport with satellite halls: a transfer demand-oriented approach.

School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China

Wenliang Zhou

Linhuan zhong, associated data.

The practical data used to support the case study and findings in this study are available from the corresponding author upon request.

This study focuses on managing the gate assignment in the hub airport with both main terminal and satellite halls. We first formulate the gate assignment problem (GAP) as a binary linear programming model with multi-objective functions, where the practical constraints, e.g., gate time conflict and gate compatibility, are considered. Then, we incorporate the impact of gate assignment on transfer passengers and formulate the transfer demand-oriented gate assignment problem (TGAP) as a nonlinear model. A linearization approach and a heuristic approach are designed to solve the TGAP model. A case study is conducted based on the practical data of the Shanghai Pudong International Airport, where a comparison between the results of GAP and TGAP by the two proposed approaches is demonstrated. It shows that the proposed TGAP model and solution approaches can not only enhance the service for transfer passengers but also improve the gate utilization efficiency in the hub airport.

1. Introduction

Global air passenger demand and airport construction have experienced rapid growth over the past few decades, and passenger demand still grew steady in 2019 before the COVID-19 pandemic [ 1 ]. Driven by the recovery in domestic markets in China, many hub airports have returned to pre-pandemic levels in passenger demand and flight numbers; e.g., Shanghai Pudong International Airport was handling 110,000 domestic travelers and 900 domestic flights every day in March 2021, which are both more than that of 2019 [ 2 ]. As the import part of the hub-and-spoke system in air transportation, hub airports are experiencing rapid growth in passenger demand and flight numbers. To release operating pressure and provide better airplane/flight service and passenger service, many hub airports have built satellite halls connected with terminals by underground walkways or mass rapid transit (MRT) systems. This raises new challenges for efficient operations and passenger services in the hub airport.

Gates are a scarce resource at hub airports, facing intense air traffic and passenger demand pressure [ 3 ]. The gate operation connects air traffic (timetable and airplane service), passenger service, and ground operations (including crew assignment), which makes it critical for efficient airport operations. The gate assignment problem (GAP) is to assign airplanes/flights to suitable boarding gates or the apron at the airport on an operating period (usually one day), according to the given flight timetable and airplane fleet assignment, also taking into account the airport layout, gate compatibility, airplane types, and so on. The typical objectives of GAP usually include two aspects, minimizing the operating costs and maximizing the efficiency of gate resources for airport operators; and maximizing satisfaction for passengers. The GAP has been extensively studied as one of the most important problems in the daily operations of the airport, and see [ 4 , 5 ] for a detailed literature review, and we give an overview from airport and passenger perspectives.

From the airport operator perspective, the main objectives for GAP are efficient utilization of gate resources and reducing operating costs. Since the parking positions in the apron are usually far from the terminals and passengers need to take the shuttle bus. If airplanes are assigned to the apron, it will increase the waiting time of passengers and operation costs due to potential effects on ground operations and crew assignment. The most common objective in GAP literature is to minimize the airplanes/flights assigned to the apron (Ding et al. [ 6 ], Dorndorf et al. [ 7 , 8 ], Drexl and Nikulin [ 9 ], Deng et al. [ 10 ]). Some studies aim to minimize the total delay of airplanes when the airport is busy (Lim et al. [ 11 ], Kaliszewski et al. [ 12 ]). Moreover, as Karsu et al. [ 13 ] point out, hub airports may need to handle different types of flights (domestic/international) and airplanes, so minimizing the moves/costs of towing when airplanes need to move from a gate to another is also considered in the literature (Benlic et al. [ 14 ], Kumar and Bierlaire [ 15 ], Yu et al. [ 16 ]). We consider the gate compatibility instead of the towing operation in this study to capture the matching between airplane/flight types and gate facilities. It was also modeled in the objective function of Benlic et al. [ 14 ] and Neuman and Atkin [ 17 ]. Following the conclusion that the airport controls gate use will ensure they are used most efficiently in the survey of Gillen and Lall [ 18 ], we also consider the integrated operating of gates in terminal and satellite halls in the hub airport and include minimizing the number of used gates in the objective function.

Gate assignment also affects the passenger service quality in the airport [ 11 ], and it may influence the walking distance, waiting time, and transfer service of passengers. The consideration of passengers in the literature is mainly reflected in the objective function. Hub airports in metropolitan usually have multiterminals, and it may take a lot of time/distance to get the specific gate or transfer between gates, and many studies contribute to GAP to minimize walking time/distance of passengers. Bohr [ 19 ] proposed binary linear programming to minimize the passenger walking distance and solved it by the primal-dual simplex algorithm. Karsu et al. [ 13 ] formulated a mixed-integer nonlinear programming model for GAP to minimize the total walking distance of all passengers and the number of airplanes assigned to the apron and then proposed exact and heuristic approaches to solve it. Interested readers can also refer to Drexl and Nikulin [ 9 ], Haghani and Chen [ 20 ], Dell'Orco et al. [ 21 ], and Mokhtarimousavi et al. [ 22 ]. Besides, Yan and Huo [ 23 ] and Yan and Tang [ 24 ] are focused on minimizing the total passenger waiting time in GAP. However, from the survey of Entwistle [ 25 ], over 60% of passengers plan to shop in the airport, which means minimizing the waiting time is not always the objective of passengers, at least for some of them. Daş [ 26 ] proposed a multi-objective model to increase the shopping revenues in the airport through gate assignment, by minimizing the total passenger walking distance and assigning passengers to gates near the shop. We consider the transfer time budget of passengers in this study and propose a more comprehensive way to measure service for transfer passengers. A comparison with some abovementioned major-related studies is shown in Table 1 .

Comparisons of major-related studies.

In this study, we focus on the impacts of gate assignment on the service of transfer passengers in the hub airport with satellite halls. We proposed a novel transfer demand-oriented objective function considering the transfer time budget, together with objective functions of the airport operator, to explore the trade-off between airport operations and passenger service. Besides, we propose two optimization models, namely a binary linear programming model for gate assignment problem (GAP) and a nonlinear model for transfer demand-oriented gate assignment problem (TGAP). A linearization approach and a heuristic approach are designed to solve the TGAP model, and then, case studies are performed using the data of Shanghai Pudong International Airport.

The remainder of this study is organized as follows. In Section 2 , we give a detailed description of GAP and TGAP. The corresponding mathematic models are formulated in Section 3 . Then, Section 4 develops the linearization approach and heuristic approach to solve the model. Case studies are illustrated in Section 5 , and Section 6 concludes the study.

2. Problem Statement and Assumptions

2.1. problem statement.

We consider a hub airport with the main terminal and several satellite halls, illustrated in Figure 1 . Gates are available in both the terminal and satellite halls and integrated assigned by the airport operator, denoted by g ∈ G . Passengers can travel between the terminal and satellite halls via the MRT system. As shown in Figure 1 , the airport also has parking positions for airplanes at the apron, denoted by G ′, in case an airplane could not be assigned to a gate in the terminal or satellite halls.

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.001.jpg

Example layout of the hub airport with satellite halls.

The research period in this study is denoted by [ T 1 , T 2 ] (i.e., one day or one week) and discretized into equal-length time intervals t , and let t ∈ T . Some airplanes land and take off at the airport in this period, they occupy the gates for passenger arrivals and departures, and the set of airplanes is denoted by I . The services that provided by airplanes to transport passengers between airports are called a flight . For a specific airport, one airplane serves two flights, and we assume that the related flights of each airplane and the timetable are given. In Figure 2 , we show the relationship between airplanes and related flights it serves. An airplane i ∈ I is considered, and it serves two flights: one arriving flight with arrival time a i and one departing flight with departure time d i , and it needs to occupy a gate g ∈ G or a parking position g ∈ G ′ during the time period [ a i , d i ]. Besides, the type of airplanes (wide/narrow-body) and related flights (domestic/international) are also given. Since the correspondence between aircrafts and flights is given, it is possible to model by either airplane or flight, and we use the airplane for modeling in this study.

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.002.jpg

Airplane and served flights.

As for the utilization of gates, the first thing to consider is time conflicts. As shown in Figure 3 , airplanes i ∈ I and i ′ ∈ I use the same gate consecutively, and the usage time periods of the two airplanes [ a i , d i ] and [ a i ′ , d i ′ ] should not overlap. Besides, buffer time τ b should also be satisfied between serving two airplanes for ground operations. Secondly, because gates in terminal and satellite halls may have different functions (such as check-in facilities and passport control), we consider the gate compatibility in this study. In particular, for airplanes, we consider gate compatibility for wide/narrow-body types; for related flights, the gate compatibility is associated with serving domestic/international flights.

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.003.jpg

Gate utilization and buffer time.

The gate assignment problem (GAP) is to assign airplanes i ∈ I to gates or apron g ∈ G ∪ G ′ with considering constraints such as gate time conflict constraints and gate compatibility, and the objective function mainly concerns making efficient use of gates or reducing the number of occupied gates. The decision variables are binary gate assignment variables x ig , i ∈ I , g ∈ G , and binary gate utilization variables x g , g ∈ G . In particular, x ig equals 1 if airplane i is assigned to gate g and otherwise equals 0; x g equals 1 if gate g is used by any airplane and otherwise equals 0.

We also attempt to consider the impact of gate assignment on passengers in this study. The set of passenger groups is denoted by P . As shown in Figure 4 , each group of passengers p ∈ P transfers from the same arrival flight served by airplane i p 1 ∈ I to the same departure flight served by airplane i p 2 ∈ I (which can be abbreviated as i 1 and i 2 ). The transfer time budget for passenger group p is defined as B p = d i 2 − a i 1 . The number of passengers in group p is given and denoted by n p .

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.004.jpg

Illustration for transfer time budget of passengers.

Gate assignment determines passengers' shortest transfer time, including processing time, walking time, and MRT time. It will affect passengers' transfer in the airport, especially hub airports with satellite halls. The layout in Figure 1 is taken as an example, and a passenger whose transfer time budget is 60 min is considered. Different transfer scenarios are illustrated in Table 2 when arriving and departing airplanes are assigned to different gates. In Scenario 1, both gates of arriving and departing airplanes are assigned in terminal T , and the shortest transfer time is much less than the transfer time budget B p and transfer success. Passenger transfer is more stressful in Scenario 2 because the assigned gates of two airplanes are located at T and S1, respectively. Passengers in Scenario 3 and Scenario 4 fail to transfer due to the gate assignment.

Transfer scenarios for passenger group p under different gate assignment schemes.

Thus, passengers p may fail to transfer if the shortest transfer time is too long between two gates assigned to airplanes i 1 and i 2 , i.e., exceeding the transfer time budget B p . The demand-oriented gate assignment problem (TGAP) in this study focuses on the trade-off between transfer passenger service and gate utilization efficiency in GAP when satellite halls are constructed in the hub airport.

2.2. Assumptions

To facilitate the presentation of our studied problem in this study, the following assumptions are made:

A1 : (airport layout). Considering a hub airport with one terminal, several satellite halls, and an apron, the shortest transfer time between any two gates is given. There is no limit on the number and type of airplanes that use the apron simultaneously.
A2 : (flight and airplane). Given the flight timetable in the research period, including arrival/departure time, flight types (domestic/international), and airplane types (wide/narrow-body).
A3 : (gate service). Only one airplane can use a gate at a time. All of the gates have the same buffer time τ b between serving two airplanes. The gate compatibility for flights and airplanes is considered.
A4 : (passenger demand). Since the satellite halls mainly affect transfer passengers, it is assumed that we only consider the transfer passenger demand. The quantity, associated flights, and transfer time budget of passengers are given.

3. Mathematical Formulation

In this section, we first formulate the model for GAP to clarify the resource utilization and constraints in the hub airport, and then, we propose the model for TGAP considering the service of transfer passengers in Section 3.2 .

3.1. Notations and Decision Variables

Table 3 lists general indices, sets, parameters, and variables in optimization models that appeared in this study.

Main sets, indices, parameters, and variables.

3.2. Model for GAP

In this subsection, the mathematic model of GAP is formulated to integrate using the gates in terminal and satellite halls, including constraints and multi-objective functions.

3.2.1. Constraints

The constraints of GAP usually include gate utilization and airplane service, which are next described in detail.

(1) Gate Time Conflict Constraint . A feasible gate assignment scheme should guarantee that airplanes assigned to the same gate do not overlap in time and observe the buffer time. The airplane time incidence parameter δ it is introduced, which equals 1 when a i ≤ t ≤ d i + τ b and otherwise 0. So, we have the following:

The incidence parameter δ it and assignment variable x ig associate airplanes, gates, and time.

(2) Gate Utilization Constraints . For gate g ∈ G , if it is used by any airplane, the variable x g equals 1, otherwise 0. So, we have gate utilization constraints that indicate the relationship between x ig and x g as follows:

where M is a sufficiently large positive constant.

(3) Airplane Service Constraints. Each airplane must and can only be assigned to one gate or the apron, and then:

(4) Gate Compatibility Constraints . We consider the gate compatibility in this model, because gates in the different areas of terminal and satellite halls may have different functions, which are mainly influenced by facilities and equipment. The airplane gate compatibility incidence parameter σ ig is introduced, which equals 1 if airplane i ∈ I can be served by gate g ∈ G and otherwise 0. We can derive the values of σ ig based on the given airplane and gate types.

In particular, the apron can serve all types of airplanes and σ ig =1, g ∈ G ′.

(5) Constraints for Decision Variables.

Constraints ( 5 ) and ( 6 ) are binary requirements on the decision variables.

3.2.2. Objective Function

Gates are the scarce resource in an airport, and operational efficiency depends on the utilization of this bottleneck resource. Since the hub airport has terminal and satellite halls simultaneously, the GAP aims to make efficient use of the gates in the terminal and satellite halls and minimizes the operating costs.

An airplane can use parking positions in the apron if it cannot be assigned to a gate, but the apron is usually far away from the terminal and satellite halls, and passengers need to take a shuttle bus between the terminal and the apron. This will increase the transfer time of passengers on the one hand and increase the operating costs of the airport on the other hand. To make efficient use of the gates and avoid assigning airplanes to the apron, the first objective is to minimize the number of airplanes assigned to the apron:

This is a common objective in the literature of GAP (Ding et al. [ 6 ], Dorndorf et al. [ 7 , 8 ], Drexl and Nikulin [ 9 ], Deng et al. [ 10 ]). It is equivalent to maximizing the number of airplanes assigned to gates. Furthermore, this objective can be easily extended to maximize the total usage time of gates since the dwell time of each airplane is given, but it has no significant impact on passenger service so we use the objective ( 6 ) in this study.

Besides, the GAP is multi-objective in nature and operation costs for gates are expensive (including ground operation costs), which motivates us to consider objectives more comprehensively. Apart from minimizing the apron operation, the GAP also needs to consider the objective that minimizes the number of used gates, i.e.,

In this study, we consider the objective function that consists of the number of airplanes assigned to the apron and the number of used gates and then formulate the GAP as a multi-objective optimization problem. For GAP in a hub airport with the apron, if we only consider the objective ( 6 ), gate operating hours may not be fully utilized in some situations; only consider the objective ( 7 ) is obviously not feasible either, which would assign all of the airplanes to the apron. Thus, the combination and trade-off between objectives ( 6 ) and ( 7 ) is comprehensive for GAP. We use linear weight to handle these two objectives and formulate the objective function as follows:

where α 1 and α 2 are positive weights to denote the trade-off between objectives. In particular, we can obtain a Pareto-optimal solution if α 1 and α 2 are set 1, or their values are set according to the preference of airport operators.

3.2.3. Mathematical Model for GAP

The GAP can be formulated as follows:

The mathematical model for GAP given in ( 9 ) is binary integer linear programming. The model for GAP focuses on the gate resource optimization when a hub airport has both main terminal and satellite halls, and next, we will consider the impact of GAP on passengers and further improve the model involving different stakeholders.

3.3. Model for TGAP

Gate assignment affects the service quality of passengers, especially the transfer passengers in the hub airport where both main terminal and satellite halls are providing passenger service. Passengers may take a longer time to get from the arriving flight to the departing flight gate due to the gate assignment and may even exceed the transfer time budget resulting in a failed transfer. Thus, we will incorporate the service of transfer passengers in the GAP in this section.

With the given flight timetable and transfer scheme (arriving airplane i 1 and departing airplane i 2 ) of passenger group p ∈ P , we can get the transfer time budget B p = d i 2 − a i 1 . The gates serving airplanes i 1 and i 2 are denoted as g 1 and g 2 , respectively. Given the layout of terminal and satellite halls in the airport, the shortest transfer time τ ( g 1 , g 2 ) (including processing time, walking time, and MRT time) between any two gates is also fixed.

The gate assignment will influence the shortest transfer time of passenger group p . Here, we introduce the transfer pressure to describe the airport's service level for transfer passengers. The transfer pressure is the ratio of shortest transfer time to transfer time budget, and the transfer pressure for passenger p is denoted by φ p and defined as follows:

where τ c is the total transfer time (including process time and shuttle bus time) for passengers associated with the apron. If both associate airplanes of a passenger group are assigned to gates in the terminal and satellite halls, passengers need to do a gate-gate transfer with transfer time τ ( g 1 , g 2 ); otherwise, passengers need to do a gate-apron, apron-gate, or even apron-apron transfer with transfer time τ c . Note that τ c is longer than the shortest transfer time between any two gates because the parking positions in the apron are usually far away from the terminal and satellite halls.

Then, we introduce the objective that minimizes the transfer pressure for passengers to capture the passenger service in the GAP of the hub airport; i.e.,

The GAP considering passenger transfer time budget can be formulated as follows:

The mathematical model given in ( 12 ) explicitly considers the transfer passenger service and shows the trade-off between passenger service and operating costs of GAP in the hub airport. The mathematical model ( 12 ) is nonlinear programming, and the nonlinearities come from the objective function associated with passenger, where the calculation of transfer pressure φ p is a segmentation function.

Table 4 presents the complexity of the model for GAP and TGAP. It can be seen that the model size depends on the number of gates, airplanes, passenger groups, and demand discretization (number of discretized time intervals). Suppose that there is a hub airport with 10 gates and 100 airplanes with 100 transfer passenger groups, the research period is [0 : 00 − 24 : 00]. If the discretization time interval is 5 min, there will be 120 variables and 2500 constraints in the GAP model ( 9 ). The number of variables in the TGAP model ( 11 ) is 1120, with the addition of variables related to transfer passengers.

Numbers of variables and constraints in the models.

Note: | · | represents the cardinality of a set.

4. Solution Approach

The mathematic model ( 9 ) for GAP is a binary integer linear programming and can be solved by several existing commercial solvers, such as CPLEX and Gurobi (see, e.g., Linderoth and Ralphs [ 27 ]; Atamturk and Savelsbergh [ 28 ]).

As for the mathematic model ( 12 ) for TGAP, it is nonlinear programming with linear constraints, and we next propose two approaches to solve it.

4.1. Linearization Approach

In this section, the origin nonlinear programming model ( 12 ) will be transformed into binary integer linear programming by introducing new binary variables and linear constraints.

Focusing on the nonlinear objective function of the model ( 12 ), the calculation of transfer pressure φ p is a segmentation function as shown in Eq. ( 11 ). According to the analysis of transfer time under different scenarios in Section 3.3 , only need to set the shortest transfer time associated with g ∈ G ′ as τ c , i.e., τ ( g 1 , g 2 )= τ c , g 1 ∈ G ′ or g 2 ∈ G ′ the objective that minimizes the transfer pressure in ( 12 ) could be updated as follows:

It can be observed that ( 13 ) is nonlinear because of productions of binary variables x i 1 g 1 and x i 2 g 2 , and they can be replaced by auxiliary binary variables y i 1,2 g 1,2 . Following Williams [ 29 ], the productions can be replaced by adding linear constraints:

Thus, the linearized model for TGAP considering the transfer passenger service can be formulated as follows:

Note that model ( 15 ) is linear programming and can easily be solved by commercial solvers such as CPLEX and Gurobi to find a globally optimal solution.

As shown in Table 4 , the number of auxiliary binary variables y i 1,2 g 1,2 is | P | × (| G |+1) 2 . Based on the example in Section 3, there will be 121120 variables and 489000 constraints in the linearized model ( 15 ) for TGAP. Besides, when the number of passenger groups and airport gates increases, the number of variables and constraints increases rapidly, which takes a long computation time to solve the TGAP with commercial solvers. To address this issue, we further design a heuristic approach to solve the TGAP.

4.2. Heuristic Approach

The gate assignment problem is a complex nondeterministic polynomial hard (NP-hard) problem due to the complex layout of airports, multi-flights, passenger trips, and gate compatibility [ 30 , 31 ], and many studies adopted heuristic approaches to solve it [ 16 , 21 , 24 ]. To solve TGAP in large hub airports requires an efficient algorithm to obtain a satisfactory solution and solve the problem in reasonable CPU time. The simulated annealing (SA) algorithm is a metaheuristic to approximate the global optimization and has good robustness. Thus, we propose an approach for TGAP at large hub airports based on the framework of the SA algorithm.

In Algorithm 1, we adopt the following strategies to adjust the assignment scheme and get neighborhood solutions.

4.2.1. Initial Solution

The model ( 11 ) for TGAP has the same constraints as the model ( 9 ) for GAP, and we can use the optimal solution of model ( 9 ) as the initial solution. It is already efficient in terms of gate resource utilization. As for the model scales, note that the model ( 9 ) can be decomposed into two subproblems by gate compatibility on airplane types: one assignment for wide-body airplanes and associate gates and another for narrow-body airplanes and associate gates. In this way, the initial solution of TGAP is designed.

4.2.2. Passenger Service Adjustment Strategy

The passenger service adjustment strategy aims to reduce the transfer pressure of passengers, which includes three options: insert option, swap operation, and remove operation. These operations are performed sequentially, with only one of them executed in each loop, and detailed options are shown as follows.

In the current solution, we already know the gate assignment scheme, i.e., the specific gate of each airplane. As we know the transfer information of passenger group p ∈ P , P i 1 and P i 2 are denoted as the subset of passenger groups whose arriving and departing airplane is i ∈ I , respectively. Then, we denote φ i as the total transfer pressure of passengers associated with airplane i , which is given as follows:

The total transfer pressure of each airplane is calculated by equation ( 16 ) and does the following operations: ① the airplane that has the maximum transfer pressure and is already assigned to a gate is selected. ② Insert operation: the subset of gates that has available time and already been used is found, the selected airplane is inserted into one of them randomly. If the subset is empty, the next operation is proceeded. ③ Swap operation: the subset of airplanes that has the same time interval and type (no violation of constraints ( 1 ) and ( 4 )) is found, the selected airplane and one of them are randomly swapped. If the subset is empty, the next operation is proceeded. ④ Remove operation: if the above two operations are not executed, the subset of gates suitable for the selected airplane is found, one of them is chosen at random, and the selected airplane is assigned to this gate, and then, the airplanes with conflicts are assigned to the apron.

4.2.3. Gate Utilization Strategy

The gate utilization strategy concentrates on reducing the number of used gates, which includes two options: insert option and remove operation. Based on the current solution, we could know the set of airplanes assigned to gate g ∈ G and denoted by I g . u g is denoted as the time utilization ratio of gate g , which equals the ratio of occupied time to research period:

To reduce the number of used gates, the following operations are executed: ①the gate with the lowest time utilization ratio using ( 17 )is found, and if the ratio is lower than a threshold value (such as 40%), then next options are done. ② insert operation: for airplanes assigned to the selected gate, the subset of gates that has available time and already used is found, and these airplanes are inserted in one of them randomly. ③ Remove operation: the rest of the airplanes are assigned to the apron after the previous option.

4.2.4. Apron Airplane Adjustment Strategy

The apron airplane adjustment strategy is designed to reduce the number of airplanes assigned to the apron and transfer pressure of passengers. Thus, included operations in this part of the strategy consider both of the two objectives. ① The total transfer pressure of each airplane in the apron by ( 16 )is calculated and the maximum one of them is found. ② The insert and swap operations are executed. This airplane is randomly inserted into a gate that has available time periods; otherwise, it is swapped with another airplane assigned to a gate without violation of constraints ( 1 ) and ( 4 ). ③ If the above two operations are not satisfied, one of the airplanes in the apron is attempted insert into an available gate. ④ If there is any available empty gate, the airplane with the shortest overlap time period with other airplanes in the apron is found and assigned to the new gate.

The proposed heuristic approach shown in Algorithm 1 is based on the framework of the SA algorithm, together with three strategies for improving different parts of objective functions, and it would find a satisfactory solution of TGAP in the model (13).

5. Case Studies

To demonstrate the performance of the proposed models and solve approaches, we use the data of the Shanghai Pudong International Airport in China as a case study. We will describe the experiment data in Section 5.1 . The numerical results are presented in Section 5.2 .

All numerical tests are conducted on a personal computer with Intel® Core (™) 3.00 GHz processor and 16.00 GB RAM and Windows 10 Home Edition Operating System (64 bit). The YALMIP-R20190425 together with MATLAB R2019b is used to conduct the numerical tests. The commercial solver Gurobi optimization studio 8.1.1 (with academic license) is adopted to solve GAP and linearized TGAP models, and the solver used the branch-and-cut algorithm to find optimal solutions for the above two mixed-integer programming models.

5.1. Data and Parameter Setting

We consider a real-world case study on the Shanghai Pudong International Airport, which is an important hub airport in eastern China. The gates are integrated used in a terminal T and connected satellite hall S, and both terminal T and satellite hall S could handle transfer processes for passengers. There is an MRT line that connects T and S to quickly transport passengers, assuming that passengers' MRT time for a one-way trip is 5 minutes (for layout, see Figure 5 ). We consider the GAP and TGAP for 28 gates in terminal T and 41 gates in satellite hall S and an apron, and the detailed information of gates can be found in Table 5 . The compatibility of gates such as service for domestic/international flights and wide/narrow-body airplanes is also given. τ c is set as 180 minutes.

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.005.jpg

Layout of terminal T1 and satellite hall S1 in Shanghai Pudong International Airport.

Gate information and compatibility in the case study.

Note. T: terminal, S: satellite hall, D: domestic flight service, I: international flight service, W: wide-body airplane service, N: narrow-body airplane service.

In this case study, the considered research period is set as [0 : 00 − 24 : 00], which covers a full day of operations. We select 296 airplanes related to the above gates of China Eastern Airlines and Xiamen Airlines on January 20, 2018. Table 6 shows several records as an example, and every record corresponds to one airplane, which services two flights. The information of airplanes includes the arrival and departure date, arrival and departure time, arrival and departure flights, airplane types (wide/narrow-body), and flight types (domestic/international).

Partial airplane and flight records.

Note. D: domestic flight, I: international flight, W: wide-body airplane, N: narrow-body airplane.

Meanwhile, transfer information of more than 3000 passengers is selected and divided into groups based on arrival and departure flights. The example of information is shown in Table 7 , which includes arrival and departure flight, arrival and departure date, and passenger number in groups. Combining with the information of airplanes and flights in Table 6 , we can easily get the transfer time budget of each passenger group. Since the layout of the airport is set, the shortest transfer time τ ( g 1 , g 2 ) between any two gates is also determined, including processing time, walking time, and MRT time. The buffer time of gates τ b is set to 45 minutes.

Partial transfer passengers' information.

The linear weights in the model (10), (13), and (16) are, respectively, α 1 =296, α 2 =1, α 3 =10; the algorithm parameters are set as follows: T 0 =10 6 , T f =5, m max =50, θ =0.5.

5.2. Computational Results

Given the above data and settings, the proposed solution approaches will be implemented for GAP and TGAP. The results of GAP, TGAP with linearization approach, and performance comparison are shown in Subsection 5.2.1 ; Subsection 5.2.2 shows the result of TGAP solved by the heuristic approach.

5.2.1. Solutions of GAP and Linearized TGAP

In this subsection, we solve the GAP and linearized TGAP by commercial solver Gurobi optimization, and the CPU time to solve GAP is 5.75s. The result of GAP is shown in Figure 6 , and the horizontal axis represents the research period ([0 : 00 − 24 : 00] on January 20, 2018), and the vertical axis represents the total of 69 gates in terminal T and satellite hall S. The colored bars in Figure 6 represent the time period when the airplanes occupy the corresponding gates, and the buff time is not included. It can be clearly seen that airplanes satisfy the time conflict constraints of gates and buffer time is also held between two adjacent airplanes. One can find that airplanes arriving and departing during [0 : 00 − 6 : 00] usually occupy the gates for a long time as passengers tend not to travel at this period. The result of TGAP is shown in Figure 7 , which also satisfies all of the constraints.

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.006.jpg

Optimal gate assignment scheme of GAP.

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.007.jpg

Optimal gate assignment scheme of linearized TGAP.

From Figure 6 , we can find that several gates (S29, S30, S39, and S41) are not occupied by any airplane, and they are all serving wide-body airplanes. Meanwhile, Figure 8 shows that all wide-body airplanes are already assigned to gates in GAP and that ratio for narrow-body airplanes is 81%, and the total number of airplanes successfully assigned to the gates is 249. Here is why we consider the objective that minimizes the number of used gates in ( 7 ), which can improve the efficiency of gate utilization when one kind of resource is sufficient, i.e., gates for wide-body airplanes. Thus, the combination of objectives ( 6 ) and ( 7 ) is more comprehensive for gate utilization in both GAP and TGAP.

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.008.jpg

Assigned numbers and rates for different types of airplanes in GAP.

Next, we compare the solution of GAP and TGAP, and values of different parts in objective functions are shown in Table 8 , of which the value of Z 3 (transfer pressure) in GAP is calculated based on the optimal solution of the model (10) and services for passengers are not taken into account. It is obvious from Table 8 that when the first and second objectives (minimizing the number of apron airplanes Z 1 and used gates Z 2 ) are close in the GAP and TGAP, considering the objective Z 3 can significantly reduce the transfer pressure of passengers (23.64% reduction). That is, model ( 12 ) of the TGAP can improve the service level of transfer passengers without increasing the resource requirement in the hub airport. Moreover, although the number of assigned airplanes has decreased in the TGAP solution, the time utilization rate of gates in terminal T and satellite hall S has increased, and the scheme in TGAP assigns airplanes serving more transfer passengers and with less occupation time to the gates. This also demonstrates that considering the gate resource utilization and passenger service simultaneously is a more comprehensive way to address gate assignment in the hub airport.

Performance comparison of GAP and linearized TGAP solutions.

∗ Note: the total passenger transfer pressure Z 3 of GAP is calculated after solving the optimal solution.

5.2.2. Solution of TGAP Adopting Heuristic Approach

Although the proposed linearization approach in Section 4.1 can obtain the global optimal solution for TGAP, it takes a long time to converge. We adopted the commercial solver Gurobi optimization studio 8.1.1 to solve the linearized TGAP, and the CPU time to get the optimal solution is 6.09 h. The proposed heuristic approach takes 726s to solve the TGAP with the same input and parameters and only has a 2.59% solution GAP with linearization approach, and the gate assignment scheme is shown in Figure 9 as follows.

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.009.jpg

Gate assignment scheme of TGAP solved by the heuristic approach.

Passenger group no. 3 in Table 7 is taken as an example, passenger transfers from flight MU5698 arriving on January 20, 14:30, to flight MU545 departing at January 20, 16:10, and the transfer time budget is 100 min. In the GAP solution, airplanes separately serving the arrival and departure flight are assigned to gates S5 and T3. As the two gates are not in the same terminal, the shortest transfer time for passengers is 60 min. When we take the transfer pressure into account in TGAP, the above two airplanes are assigned to gates T18 and T5, which are both in the terminal T , passengers' shortest transfer time reduced to 35 min, and transfer pressure decreased from 0.60 to 0.35. Furthermore, the total transfer pressure of passengers in the solution of TGAP by the heuristic approach is still lower than that in the solution of GAP. This indicates that the TGAP well considered the service for passengers and realized the integrated assignment of gates in terminal and satellite halls.

As shown in Figure 10 , gates in the terminal T are all used due to the shorter transfer time related to the gates in T than that in S, and there are 3 gates in the satellite hall S serving wide-body airplanes that are not used. The total utilization rate of gates is 96%, and the time utilization rate is 69.98%. For TGAP, although the gate time utilization rate in the solution of the linearized approach (70.21%) is higher than that of the heuristic approach, the difference is not significant.

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.010.jpg

Gate utilization in terminal T and satellite hall S in TGAP.

Next, we compare the passenger transfer pressure in three cases: the GAP solution, linearized approach of TGAP solution, and heuristic approach of TGAP solution. The proportion of passengers within different transfer pressure intervals of the above three solutions are reported in Figure 11 , where we observe that most of the passengers' transfer pressure remains at a relatively low level and lies in the range of [0.1, 0.5] in all three cases. Two solutions of TGAP are compared with the GAP solution, and we can see that the quantity of passengers who experience low transfer pressure ([0.1, 0.3]) in TGAP solutions is significantly more than that in the GAP solution, while in the high transfer pressure range ([0.5, 1.0]), the proportion of passengers in TGAP solutions is less than that in the GAP solution. This result indicates that proposed TGAP models could improve the service for transfer passengers.

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.011.jpg

Comparison of passenger transfer pressure.

Turning now to solutions of TGAP by linearization approach and heuristic approach, the distributions of passenger transfer pressure in these two solutions are comparable, which means the proposed heuristic approach could obtain a satisfactory solution in a reasonable time. What is striking in Figure 11 is that some passengers in all three solutions have transfer pressure greater than 1 because related airplanes are assigned to the apron, and this situation is enhanced in TGAP solutions.

6. Conclusions and Future Research

In this study, we focus on the impacts of gate assignment on the service of transfer passengers in the hub airport with satellite halls. First, a binary linear programming model for GAP is proposed that considers the gate time conflict, gate compatibility constraints, and the airport operator-oriented objective functions. Then, we introduce the transfer time budget and transfer pressure to measure the passenger service and formulate the TGAP as a nonlinear programming with linear constraints. In particular, multi-objective functions were considered in the TGAP model, including transfer demand-oriented and operator-oriented objectives. We proposed a linearization approach and a SA algorithm-based heuristic approach to solve the nonlinear model of TGAP. Finally, the case study based on practical data demonstrated the benefits of the proposed models and solution approaches. In the experimental results, it was verified that the proposed TGAP model and solution approaches can improve the service for transfer passengers and lead to more efficient utilization of gate resources in the hub airport.

Further research could consider the randomness of transfer passenger demand and the effects of random flight delay on gate assignment and transfer passenger service. We can also manage the fairness of passengers through transfer pressure in the gate assignment problem.

An external file that holds a picture, illustration, etc.
Object name is CIN2022-5087941.alg.001.jpg

Solving framework based on SA algorithm for TGAP.

Acknowledgments

This work was supported by research grants from the National Natural Science Foundation of China (grant nos. U1934216, 71871226, and U2034208) and the Fundamental Research Funds for the Central Universities of Central South University (grant no. 2019zzts272).

Data Availability

Conflicts of interest.

The authors declare that there are no conflicts of interest regarding the publication of this study.

Saved searches

Use saved searches to filter your results more quickly.

To see all available qualifiers, see our documentation .

Notifications

Mixed Integer Linear Programming (MILP) Model for solving airport gate assignment problem.

marco-cheung/airport-gate-assignment-problem

Folders and files, repository files navigation.

The stand allocation for flight arrivals and departures is an important task for airport operators at major airports. For example, as almost all COVID-19-related travel restrictions to Hong Kong has been lifted, it comes to a challenge for the airfield management to decide the minimum number of required parking stands, including frontal stands and remote stands, that can serve all scheduled passenger flights based on a given data of upcoming monthly flight schedule. In case the number of flights exceeds the capacity of gate/stands parking, i.e., overflow towards remote stand parking, then the airport operators need to evaluate how many additional remote stands, which are not equipped with passenger-boarding bridge”, are required so as to facilitate planning of airside shuttle bus schedule for in advance.

Model Selection

In this project, the Mixed-Integer Linear Programming (MILP) is applied to solve this problem. In our case, we want the model to find the feasible solution for the objective function subject to a set of constraints. A decision variable is a parameter that the model solver can adjust in its best effort to minimize the cost of objective function. Simply put, we want to run the model to assign stand j for each flight turn i, on the condition that the setting of stand allocation rules can be satisfied. Some constraints are so-called “hard-rules” for which that the model must satisfy, whereas some are “soft-rules” such as the preference of assigning flights to frontal stands instead of remote stands as much as possible. In case there is no feasible solutions, it implies that the resource input of parking stands may not enough to serve the simulated scenario of aircraft operation. At this time, we will adjust input of remote stands and then re-run the model until the optimal solution can be found.

Constraints

Each parking stand can only handle specific aircraft size

Each flight turn must be assigned to a compatible parking stand

On grounds of airfield safety operation, at least 25 minutes interval (“time gap”) in-between slot of each aircraft occupied in a parking stand. For each parking stand, therefore, no flight-to-gate assignment exercise shall be made 12.5 minutes before the scheduled time of arrival (STA) of arriving flights until 12.5 minutes after the scheduled time of departure (STD) of departing flights

Aircraft with over 6 hours of ground time (i.e. STA - STD) will be towed away from the frontal stand

If the pair of flights do not correspond to the same zone (i.e. Green-Orange or Orange-Green), then towing protocol will also be applied

For towing plane, add additional 90 mins on top of ATD to cater for boarding, departing the plane and the physical process of towing. Besides, the aircraft needs to tow back to the compatible stand to prepare for embarking 1 hour prior to its STD

Mainland China (CN) flight shall be allocated to “Green zone” (exclusively defined by a set of parking stands), where remote stands #N141 - N145 are reserved for handling departure flights in Green Zone)

Non-mainland flight shall be allocated to “Orange zone” only

Assumptions

The first arriving passenger flights were assumed to land in an empty airport, i.e., no parking stands were pre-occupied at the first beginning.
The flight schedule data is simulated as close as some days in Nov-22 flight schedule of Hong Kong International Airport (HKIA). (Due to data privacy concern, the raw data of seasonal schedule data would keep it confidential)
Our model only serves one objective in this study, despite there may have multiple objectives to consider by Airport Authority during stand allocation, such as assigning flights close to airline service counters, maintaining some degree of stand allocation patterns for regular flights from time to time, etc.

Terminology

Turns : a pair of arrival and departure flights with the same aircraft. STA : Scheduled Arrival Time. STD : Scheduled Departure Time. Ground Time : the planned time period for which an aircraft will occupy a particular stand, i.e. time between STA and STD. Frontal Stand : a set of parking stands equipped with “passenger boarding bridge” for connection with the terminal building. Remote Stand : an aircraft stand that is not airbridge-served, therefore requires require a shuttle bus service. Towing Stand : a set of parking stand for serving towing plane. Green Zone : Mainland flight shall be allocated to “Green” zone only. Orange Zone : Non-Mainland flight shall be allocated to “Orange” zone only. Towing : The turns followed by the towing protocol will be broken up as three turns as each of them is assigned to a different stand for parking: (1) disembarking in a frontal or remote stand; (2) temporary parking in a towing stand; (3) tow back to a frontal or remote stand for embarking 1 hour before STD.

Model result

The model was built with four versions . Why? Because after running the model for the first time, it could not find a feasible solution that satisfy the hard-rule of 25-minutes interval in-between each turn in the parking stand. So for 2nd model version , the model found out that the existing resource of parking stand input can only satisfy the constraints by setting 14-minutes as buffer time at most . However, this is not what the final answer that the airport management was looking for. For the 3rd model version , a feasible solution could be reached when the flight schedule related to "Green Zone" (the zone exclusively designed for handling mainland China flights) are removed. In other words, it appears that the issue lied in the question of input stand resources in "Green Zone" . In our final model version , three more remote stands (namely W121-123) were added for "Green Zone", holding all other original constraints constant. The final result of stand allocation of turns is presented in Gantt Chart for data visualization : https://marco-cheung.github.io/airport-gate-assignment-problem

Jupyter Notebook 64.5%

Guidelines to Write Experiences
Write Interview Experience
Write Work Experience
Write Admission Experience
Write Campus Experience
Write Engineering Experience
Write Coaching Experience
Write Professional Degree Experience
Write Govt. Exam Experiences
GATE Preparation Experience
JEE Exam Preparation Experience
NIMCET Exam Preparation Experience
MHT CET Preparation Experience
JEE Preparation Experience
IIT JEE Preparation Experience
JEE Main Preparation and Exam Experience
12 Board Exam Preparation Experience
GATE CS-IT Exam Experience
JEE Main Exam Experience
JEE Mains Exam Experience
GATE CS Exam Experience
JEE Main Exam Experience 1
CUET PG MCA Entrance Exam Preparation Experience
JEE Exam Experience

GATE Exam Preparation Experience

Hello! Everyone. Recently, I took the Graduate Aptitude Test in Engineering (GATE) and I’m excited to share my preparation strategy, exam day experience, and tips for future aspirants. The GATE exam is a crucial milestone for engineering students aspiring for higher studies or seeking career opportunities in Public Sector Undertakings (PSUs).

Preparation Strategy

Preparing for the GATE exam requires a blend of strategic planning, consistent effort, and the right resources. Here’s how I approached my preparation:

Understanding the Exam Pattern and Syllabus:

I started by thoroughly understanding the GATE exam pattern and syllabus. This helped me identify the core areas to focus on and the types of questions to expect.

Creating a Study Plan:

I designed a detailed study plan, dividing my time effectively between different subjects. I allocated more time to subjects I found challenging and ensured that I balanced study hours with breaks to avoid burnout.

Resources Used:

Textbooks: I referred to standard textbooks of the college syllabus for in-depth understanding.
Online Lectures: Platforms like NPTEL and YouTube were incredibly helpful for topics I found difficult to grasp from textbooks.
Previous Year Papers: Solving past years’ GATE papers was crucial in understanding the exam pattern and practising time management.
Mock Tests: Regularly taking mock tests on platforms like PW helped me gauge my preparation level and improve my speed and accuracy.

Focus on Concepts:

I emphasized understanding concepts over rote learning. This approach not only made problem-solving easier but also helped in retaining information longer.

Regular Revision:

I kept time for weekly and monthly revisions to ensure that I retained what I had studied. This also helped in identifying and strengthening weak areas.

Exam Day Experience

Preparation:.

On the day of the exam, I ensured I had a good night’s sleep and a healthy breakfast to stay energized. I reached the exam centre well in advance to avoid any last-minute rush.

Challenges Faced:

Nervousness : Despite thorough preparation, I felt nervous. To calm myself, I practised deep breathing exercises.
Time Management: Managing time effectively was crucial. I tackled the General Aptitude section first, followed by Engineering Mathematics, and then moved to the core subject questions.
Tricky Questions: I encountered a few tricky questions. Instead of spending too much time on them, I marked them for review and moved on, ensuring I answered all the questions I was confident about first.

Results and Insights

My hard work paid off and I secured a rank of 1400, with a score of 50. This opened up opportunities for higher studies at prestigious institutions and potential job offers from PSUs.

Insights for Future Test-Takers:

Start Early: Begin your preparation at least a year in advance to cover the syllabus thoroughly.
Consistent Practice : Regular practice and mock tests are key to success.
Understand, Don’t Memorize: Focus on understanding concepts rather than memorizing them.
Stay Healthy : Maintain a balanced diet and exercise regularly to keep your mind and body fit.
Stay Positive : A positive attitude and self-belief can make a significant difference in your performance.

Please Login to comment...

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

Airports confirm nationwide border issue as e-gate problems cause delays

Border Force
Tuesday 7 May 2024 at 10:56pm

The Home Office has confirmed airports across the country are being impacted by problems with UK Border Force e-gates

Travellers are facing severe delays across the UK as airports confirm a nationwide issue to border services.

Heathrow, Stanstead, Manchester, Gatwick and Glasgow airports airports have all confirmed they are impacted by problems with UK Border Force e-gates.

Images and footage shared on social media on Tuesday evening appeared to show long queues forming at the gates, which scan passports.

A Home Office spokesperson said: "We are aware of a technical issue affecting e-gates across the country.

“We are working closely with Border Force and affected airports to resolve the issue as soon as possible and apologise to all passengers for the inconvenience caused.”

Nathan Lane, a passenger stuck in Heathrow, told ITV News: “They really seemed to be having a meltdown. It seemed to be happening right when we landed. Staff were yelling and all the e-gates were down - so immediately it was clear something bad was happening.

"It took me about two hours I’d say, and we were towards the front. I can’t even imagine for the people who kept being added to the back of the queue."

Kevin Wood, who was stuck in Stanstead, told ITV News: “I believe that there has been a nationwide IT failure at Border Force.

"At Stanstead Airport I was sent back from e-passport machines to join the queue for manual checks then queue stopped and we weren’t moving as more and more people joined.

"We were patronised with repeated apology announcements regarding the failure. After an age (about an hour) we started moving as they slowly populated the booths."

Posting to X, formerly Twitter, Heathrow airport wrote: “We are aware that Border Force is currently experiencing a nationwide issue.

"Our teams are supporting Border Force with their contingency plans to help resolve the problem as quickly as possible and we apologise for any impact.”

A Manchester Airport spokesperson said: “We are aware of an issue with UK Border Force’s systems across the country, affecting a significant number of airports.

"Our Resilience Team and customer services colleagues are supporting passengers while UK Border Force and fixes the issue.”

A spokesperson for Stansted Airport said: “We are aware of an issue with UK Border Force’s systems across the country, affecting all UK airports.

“Our operational and customer service colleagues are supporting passengers while UK Border Force and the Home Office fix the issue.”

Glasgow Airport said in a post on social media: "Passengers arriving back on international flights within the next few hours may experience longer than normal waiting times at the border. This is due to a nationwide Border Force issue."

Want a quick and expert briefing on the biggest news stories? Listen to our latest podcasts to find out What You Need To Know…

Australia edition
Europe edition
International edition

E-gates back online after chaos at Heathrow and other UK airports

Home Office rules out cyber-attack as a cause of technical outage that delayed thousands of passengers at passport control

The e-gates failure that left thousands of passengers queueing at UK airports has been resolved, the Home Office has said while ruling out a cyber-attack as a cause.

Airports said passengers could expect to travel smoothly again on Wednesday after widespread delays on Tuesday evening owing to a nationwide technical outage affecting UK Border Force e-gates.

Heathrow, Gatwick, Stansted, Edinburgh, Birmingham, Manchester and Bristol airports all confirmed problems with passengers being processed through the border on Tuesday.

Border officials were left to manually process all travellers instead. Pictures shared on social media showed long queues forming at passport control at several airports.

A Home Office spokesperson said on Wednesday: “E-gates at UK airports came back online shortly after midnight.

“As soon as engineers detected a wider system network issue at 7.44pm last night, a large-scale contingency response was activated within six minutes.

“At no point was border security compromised, and there is no indication of malicious cyber-activity.” Heathrow airport’s X account also confirmed soon after midnight that systems were running as usual.

Manchester airport said a dedicated team and customer services staff were supporting passengers while UK Border Force fixed the problem.

Among delayed passengers were Sam Morter, 32, who arrived at Heathrow from Sri Lanka, said it was “pandemonium” when he got to passport control in Terminal 3, where all of the E-gates had blank screens.

He told the PA news agency: “There was a lot of Border Force officials running and scrambling around. Four or five went to man the posts and start processing the UK passports manually.

“But at the same time, hundreds of passengers started to flood into passport control, so it all of a sudden became chaotic and they couldn’t cope with the number of the people coming in.

“We weren’t given any information. There was no information on the Tannoys or from staff.”

Another affected traveller wrote on X: “Long queues at Heathrow airport where passengers are being held at arrivals for a system failure. Been here already 1 hour and the queue is only getting bigger. No communication given to anyone on what is the timeframe to sort this out.”

Another person posted video footage of the chaos and wrote: “No e-gates working. This is the current queue in Gatwick airport with lots of children and no water.”

One X user at Heathrow wrote: “Been stood here over an hour! My taxi has cancelled and at this rate I’ll be too late for a train.” Another wrote: “My daughter has been waiting in a queue for over 2 hours now after a 12-hour flight.”

There are 270 automated gates in total at 15 air and rail ports in the UK, using facial recognition to allow people to enter the country.

Passengers were already facing disrupted journeys to and from airports owing to industrial action affecting train services across the UK throughout this week until Saturday.

Border Force workers also staged a four-day strike at Heathrow in a dispute over working conditions last week.

Air transport
Heathrow airport
Gatwick airport
Business travel
Stansted airport

Early horses list and odds for the 2024 Preakness Stakes

In the 2024 Kentucky Derby , Mystik Dan defied the odds and emerged victorious by a nose in a heart-stopping photo finish. This triumph has positioned Mystik Dan as the frontrunner for the upcoming Preakness Stakes, the second leg of the prestigious Triple Crown .

The Preakness Stakes , a race steeped in history and tradition, is set to take place on May 18 at the iconic Pimlico Race Course in Baltimore. The draw for this momentous event is scheduled for May 13, marking a crucial step for the preparation in the journey towards the title.

Looking back at the previous year's Preakness Stakes, it was a memorable victory for National Treasure's jockey John Velazquez, who jockeyed Fierceness in the 2024 Kentucky Derby. Despite entering the race with 3-1 odds, National Treasure defied expectations and claimed his first Preakness Stakes victory, a testament to the unpredictable nature of this event.

With just over a week to go, the anticipation for the 2024 Preakness Stakes is building. Here are the early odds for the race, adding to the excitement around this prestigious event.

Horse racing: Sierra Leone jockey Tyler Gaffalione could face discipline for Kentucky Derby ride

2024 Preakness Stakes horses and early odds

Early odds for potential horses ahead of the draw listed below via CBS Sports:

Horse: Muth | Early odds: 10-11
Horse: Mystik Dan | Early odds: 3-1
Horse: Tuscan Gold | Early odds: 5-1
Horse: Imagination | Early odds: 5-1
Horse: Just Steel | Early odds: 10-1
Horse: Seize the Gray | Early odds: 10-1
Horse: Copper Tax | Early odds: 16-1
Horse: Uncle Heavy | Early odds: 20-1
Horse: Informed Patriot | Early odds: 20-1
Horse: Mugatu | Early odds: 33-1

Preakness Stakes 2024: TV, streaming and where to watch

When: Saturday, May 18
Coverage starts : 10:30 a.m. ET
Post time: 6:50 p.m. ET
Where: Pimlico Race Course, Baltimore, Maryland
Cable TV: NBC
Streaming: Peacock ; YouTube TV; fuboTV

How to watch: Watch the 2024 Kentucky Derby with a Peacock subscription

We occasionally recommend interesting products and services. If you make a purchase by clicking one of the links, we may earn an affiliate fee. USA TODAY Network newsrooms operate independently, and this doesn’t influence our coverage.

Gannett may earn revenue from sports betting operators for audience referrals to betting services. Sports betting operators have no influence over nor are any such revenues in any way dependent on or linked to the newsrooms or news coverage. Terms apply, see operator site for Terms and Conditions. If you or someone you know has a gambling problem, help is available. Call the National Council on Problem Gambling 24/7 at 1-800-GAMBLER (NJ, OH), 1-800-522-4700 (CO), 1-800-BETS-OFF (IA), 1-800-9-WITH-IT (IN). Must be 21 or older to gamble. Sports betting and gambling are not legal in all locations. Be sure to comply with laws applicable where you reside.

The airport gate assignment problem: mathematical model and a tabu search algorithm

Ieee account.

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests
US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support
About IEEE Xplore
Accessibility
Terms of Use
Nondiscrimination Policy
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

3 Astros takeaways: Selloff possibilities, Yordan Alvarez’s RISP problem and 2 homegrown DFAs

Houston Astros' Yordan Alvarez hits a one-run single against the Detroit Tigers in the eighth inning of a baseball game, Friday, May 10, 2024, in Detroit. (AP Photo/Paul Sancya)

As he is prone to do, Justin Verlander strapped the Houston Astros to his back on Sunday before Houston’s bats finally backed up its ace during a 9-3 win against the Detroit Tigers at Comerica Park.

The victory secured a series win and salvaged a 3-3 road trip. A critical 10-game homestand looms for a Houston team still seeking some stability. Seven of the 10 games are against American League West opponents, offering the club its best chance at gaining ground from the cellar.

Here are three takeaways from the road trip:

Dana Brown must consider all options

Most clubhouses have at least one television always tuned to MLB Network, so general manager Dana Brown had no choice but to back a ballclub that may be listening. Saying he “can’t predict any scenario” in which the Astros sell at the trade deadline is one of the only acceptable responses to a question that isn’t going away, even after a respectable road trip.

"No. No, I can't envision that. This ballclub is too good." – Astros GM Dana Brown on if he envisions a scenario where the team is a seller #MLBNow | #Relentless pic.twitter.com/0kEEULJUoW — MLB Now (@MLBNow) May 7, 2024

Since 1901, only seven teams have started a season 15-25 and finished with a winning record. Just three of them made the playoffs, though a third wild-card spot and an underperforming American League West give the Astros more hope than other teams mired in their situation.

Brown shouldn’t declare the season dead on May 7, but refusing to acknowledge the possibility of a selloff — even privately — is almost as misguided. Before the season, both The Athletic’s Keith Law and MLB Pipeline ranked Houston’s farm system 27th out of 30 teams. Infusing more talent is crucial, and part of the reason owner Jim Crane hired Brown in the first place.

Nothing Crane has done during his ownership tenure suggests he’s about to offer the type of extension it will take to retain Kyle Tucker , who launched his league-leading 13th home run on Sunday. Alex Bregman ’s brutal start also won’t stop agent Scott Boras from seeking the sort of deal Crane has never been willing to give.

If the Astros can’t engineer a turnaround and enter July with realistic playoff chances, it would be a dereliction of duty not to dangle one or both of those players during the trade deadline. Pitchers Verlander and Ryan Pressly are obvious candidates, too, but both have no-trade clauses in their contracts and would prefer to stay in Houston.

Since his franchise’s golden era began, Crane has reiterated: “While I’m here, the window is always going to be open.” His stance means much more than anything Brown will utter across the next three months.

Barring a total collapse, it’s difficult to envision Crane softening on such a strong statement and conceding, especially while carrying the largest payroll during his ownership tenure. He resides in a city and owns a franchise well-versed in this exact situation. The last team to start 15-25 and make the playoffs? The 2005 National League champion Houston Astros.

Yordan Alvarez ’s run production problem

Since April 10, Alvarez has taken 33 plate appearances with runners in scoring position. He has one hit: a game-tying single down the right-field line during Friday’s 5-2 win against the Tigers.

Among Astros, only Yainer Diaz has taken more at-bats than Alvarez with runners in scoring position across that 28-game span. Since it began, Alvarez’s OPS has plummeted 259 points from 1.038 to .779. It never dipped below .895 last season.

The situation epitomizes the Astros’ first 40 games. Houston has the sport’s second-highest batting average but has been outscored by 13 other teams. Getting the club’s best hitter a bevy of at-bats with runners in scoring position should be its foremost goal. The Astros are accomplishing it — and Alvarez isn’t coming through.

Entering Sunday, Alvarez had a minus-5 batter run value with runners in scoring position, according to Baseball Savant. Last season, it was 28. Alvarez is hitting .157 with runners in scoring position this season. A .231 expected batting average suggests there isn’t much bad luck involved, either.

Dissecting Alvarez’s issues with runners in scoring position is difficult and also arrives with the caveat of a small sample size. His 15.2 percent whiff rate with runners in scoring position is lower than his 22.9 percent career clip while his 92.8 mph average exit velocity is in line with his 93 mph season average. A 39.4 percent hard-hit rate, however, is far lower than his usual clip.

All season, manager Joe Espada has bemoaned a lack of plate discipline during run-scoring situations. Entering Sunday’s game, Alvarez had swung at 27 of the 87 pitches he saw out of the strike zone with runners in scoring position — a 31 percent clip almost identical to his overall 30.8 percent chase rate this season. That, it should be noted, is elevated from Alvarez’s career 26.6 percent rate.

Last season, Alvarez had a 28.4 chase rate with runners in scoring position. Perhaps he, like so many in this lineup, is feeling the pressure of a poor start and trying to compensate. All 176 of Alvarez’s plate appearances have arrived from either the second or third spots in the batting order. Espada isn’t about to move him, either, putting the onus on a preseason MVP candidate to discover a way to clutch up.

Farewell to two homegrown success stories?

The Astros drafted Corey Julks and Brandon Bielak three rounds apart in 2017, part of a 42-man draft class that’s already produced 13 major-league players. Neither signed for more than $150,000 and both were overshadowed by more noticeable names taken beforehand.

Both Julks and Bielak ascended to viable major-league players: a testament to Houston’s amateur scouting and player development. The team designated both players for assignment this weekend and each stands a decent chance of catching on with another club, be it via a waiver claim or small trade.

Julks grew up in The Woodlands, a Houston suburb, before playing three seasons at the University of Houston. His inclusion on the team’s Opening Day roster last season — and subsequent April success — represented a feel-good story for both the franchise and the city that houses it.

Still, Julks is 28 and sported a career .650 OPS against major-league pitching. Joey Loperfido passed him on the organization’s outfield hierarchy, Pedro León is threatening to do the same, and top prospect Jacob Melton may be in Triple A soon.

At full strength, Houston already has two right-handed hitting outfielders on its 26-man roster: Chas McCormick and Jake Meyers . Utilityman Mauricio Dubón ’s increased time in the outfield only furthered a logjam Julks could not crack.

Julks has outperformed at Triple A and has two minor-league option years remaining, which will increase his value to 29 other teams. Teams are always searching for pitching depth and Bielak’s respectable numbers at the major-league level — a 4.65 ERA in 191 2/3 innings — may entice some clubs.

However, Bielak is out of minor-league options, one of the primary reasons he made Houston’s Opening Day roster as a swingman. The team is already bereft of pitching depth and did not want to risk losing Bielak on waivers so early in the season.

Activating Cristian Javier on Saturday forced them to confront that scenario. The Astros could have optioned Shawn Dubin to Triple A and kept Bielak on the major-league roster, but Dubin’s ability to handle multiple innings with higher-upside stuff must have appealed to an Astros team that’s already put Dubin in a few high-leverage situations this season.

Bielak never got those chances, perhaps the first sign that his days were numbered. José Urquidy ’s impending return from the injured list could force either Spencer Arrighetti or Hunter Brown to the bullpen, too, taking the place Bielak once occupied.

(Photo of Yordan Alvarez: Paul Sancya / Associated Press)

Get all-access to exclusive stories.

Subscribe to The Athletic for in-depth coverage of your favorite players, teams, leagues and clubs. Try a week on us.

Chandler Rome is a Staff Writer for The Athletic covering the Houston Astros. Before joining The Athletic, he covered the Astros for five years at the Houston Chronicle. He is a graduate of Louisiana State University. Follow Chandler on Twitter @ Chandler_Rome

COMMENTS

A review on airport gate assignment problems: Single versus multi
The gate assignment problem. The gate assignment problem is defined on a set of m gates, an apron denoted as gate 0 and a set of n aircraft. Each aircraft i = 1, ⋯, n has an arrival time a i and a departure time d i. An aircraft usually serves two consecutive flights: an arriving flight and a departing flight.
The airport gate assignment problem: A Branch-and-Price Approach for
The airport gate assignment problem (AGAP) is a critical issue in airport operation management. When the number of flights is large, solving this situation is challenging. Therefore, designing an efficient gate assignment methodology is very important. This paper considers several significant constraints and aims to maximize the number of ...
Airport gate assignment problem with harbor constraints based on Branch
The airport gate assignment problem (AGAP) is the main problem in airport resource scheduling. It can be described as follows: arrange gates for all aircraft in the airport reasonably in the future planning period to ensure that each aircraft can park at an appropriate gate and achieve optimization objectives such as the optimal overall efficiency.
[PDF] Multi-Objective Airport Gate Assignment Problem in Planning and
The airport flight-to-gate assignment problem is solved using two methods: a linear programming relaxation of an integer program formulation and a heuristic, which indicates that the original assignment had a 32% higher average per passenger walking distance than the minimum possible distance given by the LP solution.
A Bi-Objective Constrained Robust Gate Assignment Problem: Formulation
The gate assignment problem (GAP) aims at assigning gates to aircraft considering operational efficiency of airport and satisfaction of passengers. Unlike the existing works, we model the GAP as a bi-objective constrained optimization problem. The total walking distance of passengers and the total robust cost of the gate assignment are the two objectives to be optimized, while satisfying the ...
Optimization of multi-objective airport gate assignment problem
The Airport Gate Assignment Problem (AGAP) deals with assigning a set of aircraft to the gates at airports with respect to several operational and commercial constraints. In this study, we model the AGAP as a multi-objective optimization problem that minimizes the aircraft taxiing costs and passenger walking distance. An additional ...
Multi-Objective Gate Assignment Problem for an Airport
Airport Gate Assignment (AGA) for arriving aircraft is an important aspect of airport operations that has profound impact on the other downstream processes in the airport. Aircraft is often assigned a gate based on their scheduled arrival time which ensures smooth ground operations if everything runs according to plan. Unfortunately, last minute re-assignments happen very frequently to account ...
Whale Optimization Algorithm for Airport Gate Assignment Problem
The gate assignment problem is one of the earliest ground problems studied in order to optimize airport revenue and offer the maximum service needed to each aircraft. The problem was proven to be NP-hard since 1979 . Based on the complexity of the problems handled, the solution approaches used in the literature vary from deterministic ...
The Airport Gate Assignment Problem: A Survey
The airport gate assignment problem (AGAP) is one of the most important problems operations managers face daily. Many researches have been done to solve this problem and tackle its complexity. The objective of the task is assigning each flight (aircraft) to an available gate while maximizing both conveniences to passengers and the operational efficiency of airport.
Airport Gate Assignment Problem considering Connecting Passengers
An efficient gate assignment method is necessary to solve the problem. We make two optimization models, aims to improve gate utilization and reduce connection time for connecting passengers. Considering that the airport gate assignment problem (AGAP) is NP-hard, we use genetic algorithms with data preprocessing (DP-GA) to solve the problem.
(PDF) Airport Gate Assignment problem: Mathematical formulation and
The airport gate assignment problem (AGAP) is one of the most important problems operations managers face daily. Many researches have been done to solve this problem and tackle its complexity. The objective of the task is assigning each flight (aircraft) to an available gate while maximizing both conveniences to passengers and the operational ...
[PDF] Solving the flight gate assignment problem using dynamic
A dynamic programming approach is presented that solves the flight assignment problem in linear time with respect to the number of flights and gates for a fixed number of gates. This paper considers the problem of assigning flights to airport gates—a problem which is NP-hard in general. We focus on a special case in which the maximization of flight/gate preference scores is the only objective.
Airport Gate Assignment: New Model and Implementation
Modeling To model the gate assignment problem in the mathematical form, we first describe the following parameters and data sets. Gates Set (Resources): G = {g1, g2, · · · , gc} where c is the number of available gates; Aircrafts Set (Consumers): F = {f1, f2, · · · , fn} where n is the number of aircrafts. For every aircraft fi (1 ≤ i ...
A column generation-based algorithm for gate assignment problem with
The gate assignment is a complicated real-world problem occurring in airports. The present paper studies the gate assignment problem with combinational gates. The problem is multi-objective by nature, concerning four objectives. Among those objectives, the number of passengers allocated to nearby gates is aimed to be maximized.
Optimization of multi-objective airport gate assignment problem
The Airport Gate Assignment Problem (AGAP) deals with assigning a set of aircraft to the gates at airports with respect to several operational and commercial constraints. In this study, we model the AGAP as a multi-objective optimization problem that minimizes the aircraft taxiing costs and passenger walking distance. An additional consideration is taken regarding the fairness of assignment ...
Managing the Gate Assignment Problem in the Hub Airport with Satellite
The gate assignment problem is a complex nondeterministic polynomial hard (NP-hard) problem due to the complex layout of airports, multi-flights, passenger trips, and gate compatibility [30, 31], and many studies adopted heuristic approaches to solve it [16, 21, 24]. To solve TGAP in large hub airports requires an efficient algorithm to obtain ...
(PDF) The Airport Gate Assignment Problem
The Airport Gate Assignment Problem. H. Ding , A. Lim , B. Rodrigues and Y. Zhu. Dept of Computer Science, National Univ ersity of Singapore, 3 Science Dri ve 2, Singapore 117543. Dept of IEEM ...
marco-cheung/airport-gate-assignment-problem
In this project, the Mixed-Integer Linear Programming (MILP) is applied to solve this problem. In our case, we want the model to find the feasible solution for the objective function subject to a set of constraints. A decision variable is a parameter that the model solver can adjust in its best effort to minimize the cost of objective function.
GATE Exam Preparation Experience
The GATE exam is a crucial milestone for engineering students aspiring for higher studies or seeking career opportunities in Public Sector Undertakings (PSUs). Preparation Strategy. Preparing for the GATE exam requires a blend of strategic planning, consistent effort, and the right resources. Here's how I approached my preparation:
Solving airport gate assignment problem using Genetic Algorithms
In this paper we study the assignment of the arriving aircrafts to the available gates using the fixed daily schedule. We introduce a new approach based on Genetic Algorithms (GA) to solve the gate assignment problem (GAP). The encoding strategy consists in representing the chromosome by a vector of integers.
Airports confirm nationwide border issue as e-gate problems cause
The Home Office has confirmed airports across the country are being impacted by problems with UK Border Force e-gates. Travellers are facing severe delays across the UK as airports confirm a ...
E-gates back online after chaos at Heathrow and other UK airports
Heathrow, Gatwick, Stansted, Edinburgh, Birmingham, Manchester and Bristol airports all confirmed problems with passengers being processed through the border on Tuesday.
Red Lobster closures: See full list of closed restaurant locations
Red Lobster suffered big losses with 'Ultimate Endless Shrimp' promotion. In 2023, the seafood chain's "Ultimate Endless Shrimp" deal became more popular than expected, inadvertently becoming a ...
Preakness Stakes Field: Early Horse list and odds ahead of draw
The 2024 Preakness Stakes field is beginning to take shape. Here is the list of protentional participants and their early odds to win.
How MSNBC's Leftward Tilt Delivers Ratings, and Complications
MSNBC placed a big bet on becoming comfort TV for liberals. Then it doubled down. Time slots on the cable network once devoted to news programming are now occupied by Trump-bashing opinion hosts.
A new formulation and an effective matheuristic for the airport gate
We consider the airport gate assignment problem recently addressed in Karsu et al. (2021), which lexicographically minimizes the number of aircraft assignments to apron and the total passenger walking distance. We propose a novel flow-based mixed integer linear programming formulation for the problem and a matheuristic, which is based on the ...
The airport gate assignment problem: mathematical model and a tabu
Considers an airport gate assignment problem that dynamically assigns airport gates to scheduled flights based on passengers' daily origin and destination flow data. The objective of the problem is to minimize the overall connection times during which passengers walk to catch their connection flights. We formulate this problem as a mixed 0-1 quadratic integer programming problem and then ...
Selloff possibilities, Yordan Alvarez's RISP problem and 2 homegrown
Yordan Alvarez's run production problem Since April 10, Alvarez has taken 33 plate appearances with runners in scoring position. He has one hit: a game-tying single down the right-field line ...