Real-Time Gate Reassignment Based on Flight Delay Feature in Hub Airport

Appropriate gate reassignment is crucially important in efficiency improvement on airport sources and service quality of travelers. The paper divides delay flight into certain delay time flight and uncertain delay time flight based on flight delay feature. The main objective functions of model are to minimize the disturbance led by gate reassignment in the case of certain delay time flight and uncertain delay time flight, respectively. Another objective function of model is to build penalty function when the gate reassignment of certain delay time flight influences uncertain delay time flight. Ant colony algorithm (ACO) is presented to simulate and verify the effectiveness of the model. The comparison between simulation result and artificial assignment shows that the result coming from ACO is obvious prior to the result coming from artificial assignment. The maximum disturbance of gate assignment is decreased by 13.64%, and the operation time of ACO is 118 s. The results show that the strategy of gate reassignment is feasible and effective

The comparison of the metaheuristic algorithms performances on airport gate assignment problem

Bu çalışma, 05-07 Eylül 2016 tarihleri arasında İstanbul[Türkiye]’da düzenlenen 19. European-Operational-Research-Societies Working Group on Transportation Meeting (EWGT)’da bildiri olarak sunulmuştur.The airport gate assignment problem (AGAP) is an important research area in air transportation planning and optimization. In this paper we study the airport gate assignment problem where the objectives are to minimize the number of ungated flights and the total walking distances. In order to solve the problem, we proposed a new tabu search (TS) algorithm which uses a probabilistic approach as an aspiration criterion. We compared two metaheuristics, namely, TS, and simulated annealing (SA). A greedy algorithm used as a benchmark. We compared the performances of the algorithms and analyzed at different problem sizes. Experimentations showed that the new proposed metaheuristic algorithm gave promising results.EURO Working Grp TransportatEMAY Int Eng & Consultancy Incİstanbul Teknik ÜniversitesiTürkiye Bilim ve Teknoloji Konsey

Ant Colony Algorithm and Simulation for Robust Airport Gate Assignment

Airport gate assignment is core task for airport ground operations. Due to the fact that the departure and arrival time of flights may be influenced by many random factors, the airport gate assignment scheme may encounter gate conflict and many other problems. This paper aims at finding a robust solution for airport gate assignment problem. A mixed integer model is proposed to formulate the problem, and colony algorithm is designed to solve this model. Simulation result shows that, in consideration of robustness, the ability of antidisturbance for airport gate assignment scheme has much improved

Robustness Algorithms for the Airport Gate Assignment Problem

Assigning commercial flights to available airport gates can have a major impact on the efficiency of flight schedules as well as on the level of passenger satisfaction with the service. These assignments also depend on the service requirements of flights and the capacity of stand facilities. Unexpected changes also called perturbations, like those due to air traffic delays, severe weather conditions, or equipment failures, may disrupt the initial assignments and increase the difficulty of maintaining smooth operations, which will detrimentally affect costumer satisfaction. The provision of solutions which reduce the potential detrimental effect of perturbations in the stands already assigned on the day of operation is desirable and some approaches are presented here, and compare between them to help identify their performance and trends

Contribution à la Gestion des Opérations de la Sûreté Aéroportuaire : Modélisation et Optimisation

L'objectif principal de cette thèse consiste à apporter une contribution méthodologique à la gestion de la sûreté et de coût de la sûreté aéroportuaire. Nous avons proposé un modèle logique du système de contrôle du flux des passagers au départ dans un aéroport. La finalité de ce modèle a permis de tester différents scénarios d'attaque du système, d'analyser le comportement du système dans ces conditions et d'en évaluer la perméabilité. Nous nous sommes ensuite intéressé à l'évaluation du système de contrôle des flux de passagers à l'embarquement au travers d'une approche probabiliste. Ceci conduit à la formulation de problèmes d'optimisation permettant d'améliorer les performances du système de contrôle. Afin d'obtenir la modélisation mathématique des flux de passagers au départ dans une aérogare, nous avons introduit les facteurs temps et espace par rapport au modèle précédent afin de le rendre plus proche de l'opérationnel. Enfin, nous nous sommes intéressés à l'optimisation des affectations des ressources matérielles et humaines destinées au contrôle du trafic de passagers. Le modèle spatio-temporel développé précédemment est mis à profit pour quantifier de façon dynamique, l'attribution des ressources matérielles et humaines au niveau de l'inspection filtrage et surtout d'améliorer la qualité de service. Beaucoup reste à faire dans ce domaine et le développement d'outils de modélisation, d'analyse et d'aide à la décision tels que ceux qui ont été esquissés dans ce mémoire semble s'imposer pour assurer l'optimisation de l'affectation des ressources de la sûreté aéroportuaire et ainsi garantir non seulement la qualité du service de contrôle mais aussi le niveau de coûts en résultant. ABSTRACT : Since the tragic events of September 11, aviation security is jeopardized. Many measures were taken both from the standpoint of improved procedures for improving the performance of security officers and equipment safety. Despite the implementation of new procedures and new measures, many events have shaken the airport security system established by ICAO, these include, the latest of which is the attempt acts of unlawful interference in December 2009. The main objective of this thesis is to contribute to methodological constraints security management and cost of airport security. To fight effectively against illegal acts, the standard 4.4.1 of Schedule 17 to the Chicago Convention states: “Each Contracting State shall establish measures to ensure that passengers from flights of commercial air transport and their cabin baggage are subjected to screened before boarding an aircraft departing from an area of security restricted”. The security check is then one of the highlights of airport security. We therefore propose a model system logic to control the flow of departing passengers at an airport. The purpose of this model was used to test different scenarios of attack system, analyze system behavior under these conditions and to assess permeability. We are then interested in evaluating the system of controlling the flow of passengers boarding through a probabilistic approach. This then leads to the formulation of optimization problems to improve the performance of the control system. It is then possible to establish operational procedures leading to improved system performance of passenger screening. To obtain the mathematical modeling of flow of departing passengers in a terminal, we introduced the factors of time and space relative to the previous model to make it closer to the operational. In this model, which adopts a network structure to describe the process and the transfer of passengers between the terminal sites, in particular allows to represent queues and waiting times inflicted on passengers. Finally, we are interested in optimizing the allocation of human and material resources for the control of passenger traffic. The spatio-temporal model developed previously is used to quantify dynamically allocating human and material resources at the security check and especially to improve the quality of service. This optimization allows us to formulate effective policies to manage the short term. Finally, modeling the performance of safety was performed according to a probabilistic point of view and then a dynamic perspective and space. In both cases the optimization problems were formulated based on the determination of operational parameters to improve system performance. Much remains to be done in this area and the development of tools for modeling, analysis and decision support such as those outlined in this paper seems to be necessary to ensure optimal allocation resources for airport security and so ensure not only quality service but also control the level of the resulting costs

Integrated and joint optimisation of runway-taxiway-apron operations on airport surface

Airports are the main bottlenecks in the Air Traffic Management (ATM) system. The predicted 84% increase in global air traffic in the next two decades has rendered the improvement of airport operational efficiency a key issue in ATM. Although the operations on runways, taxiways, and aprons are highly interconnected and interdependent, the current practice is not integrated and piecemeal, and overly relies on the experience of air traffic controllers and stand allocators to manage operations, which has resulted in sub-optimal performance of the airport surface in terms of operational efficiency, capacity, and safety. This thesis proposes a mixed qualitative-quantitative methodology for integrated and joint optimisation of runways, taxiways, and aprons, aiming to improve the efficiency of airport surface operations by integrating the operations of all three resources and optimising their coordination. This is achieved through a two-stage optimisation procedure: (1) the Integrated Apron and Runway Assignment (IARA) model, which optimises the apron and runway allocations for individual aircraft on a pre-tactical level, and (2) the Integrated Dynamic Routing and Off-block (IDRO) model, which generates taxiing routes and off-block timing decisions for aircraft on an operational (real-time) level. This two-stage procedure considers the interdependencies of the operations of different airport resources, detailed network configurations, air traffic flow characteristics, and operational rules and constraints. The proposed framework is implemented and assessed in a case study at Beijing Capital International Airport. Compared to the current operations, the proposed apron-runway assignment reduces total taxiing distance, average taxiing time, taxiing conflicts, runway queuing time and fuel consumption respectively by 15.5%, 15.28%, 45.1%, [58.7%, 35.3%, 16%] (RWY01, RWY36R, RWY36L) and 6.6%; gated assignment is increased by 11.8%. The operational feasibility of this proposed framework is further validated qualitatively by subject matter experts (SMEs). The potential impact of the integrated apron-runway-taxiway operation is explored with a discussion of its real-world implementation issues and recommendations for industrial and academic practice.Open Acces

An improved tabu search for airport gate assignment.

Kwan, Cheuk Lam.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 115-118).Abstract also in Chinese.Chapter 1 --- Introduction --- p.9Chapter 1.1 --- The Gate Assignment Problem --- p.9Chapter 1.2 --- Contributions --- p.10Chapter 1.3 --- Formulation of Gate Assignment Problem --- p.11Chapter 1.4 --- Organization of Thesis --- p.13Chapter 2 --- Literature Review --- p.15Chapter 2.1 --- Introduction --- p.15Chapter 2.2 --- Formulations of Gate Assignment Problems --- p.15Chapter 2.2.1 --- Static Gate Assignment Model --- p.16Chapter 2.2.1.1 --- Total Passenger Walking Distance --- p.17Chapter 2.2.1.2 --- Waiting Time --- p.20Chapter 2.2.1.3 --- Unassigned Flights --- p.21Chapter 2.2.2 --- Stochastic and Robust Gate Assignment Model --- p.22Chapter 2.2.2.1 --- Idle Time --- p.22Chapter 2.2.2.2 --- Buffer Time --- p.23Chapter 2.2.2.3 --- Flight Delays --- p.23Chapter 2.2.2.4 --- Gate Conflicts --- p.24Chapter 2.3 --- Solution Methodologies --- p.25Chapter 2.3.1 --- Expert System Approaches --- p.25Chapter 2.3.2 --- Optimization --- p.27Chapter 2.3.2.1 --- Exact Methods --- p.27Chapter 2.3.2.2 --- Heuristic Approaches --- p.28Chapter 2.3.2.3 --- Meta-Heuristics Approaches --- p.29Chapter 2.3.2.4 --- Tabu Search and Path Relinking --- p.31Chapter 2.4 --- Current Practice of Gate Assignment Problems --- p.32Chapter 2.5 --- Summary --- p.32Chapter 3 --- Tabu Search --- p.34Chapter 3.1 --- Introduction --- p.34Chapter 3.2 --- Mathematical Model --- p.34Chapter 3.3 --- Principles of Tabu Search --- p.36Chapter 3.4 --- Neighborhood Structures --- p.38Chapter 3.4.1 --- Insert Move --- p.38Chapter 3.4.2 --- Exchange Move --- p.39Chapter 3.5 --- Short Term Memory Structure --- p.41Chapter 3.6 --- Aspiration Criterion --- p.42Chapter 3.7 --- Intensification and Diversification Strategies --- p.43Chapter 3.8 --- Tabu Search Framework --- p.45Chapter 3.8.1 --- Initial Solution --- p.45Chapter 3.8.2 --- Tabu Search Algorithm --- p.46Chapter 3.9 --- Computational Studies --- p.52Chapter 3.9.1 --- Parameters Tuning --- p.52Chapter 3.9.1.1 --- Fine-tuning a Tabu Search Algorithm with Statistical Tests --- p.53Chapter 3.9.1.2 --- Tabu Tenure --- p.54Chapter 3.9.1.3 --- Move Selection Strategies --- p.56Chapter 3.9.1.4 --- Frequency of Exchange Moves --- p.59Chapter 3.9.2 --- Comparison the Fine-tuned TS with original TS --- p.62Chapter 3.10 --- Conclusions --- p.63Chapter 4 --- Path Relinking --- p.65Chapter 4.1 --- Introduction --- p.65Chapter 4.2 --- Principles of Path Relinking --- p.65Chapter 4.2.1 --- Example of Path Relinking --- p.66Chapter 4.3 --- Reference Set --- p.68Chapter 4.3.1 --- Two-Reference-Set Implementation --- p.71Chapter 4.3.1.1 --- Random Exchange Gate Move --- p.72Chapter 4.4 --- Initial and Guiding Solution --- p.73Chapter 4.5 --- Path-Building Process --- p.74Chapter 4.6 --- Tabu Search Framework with Path Relinking --- p.78Chapter 4.6.1 --- Computational Complexities --- p.82Chapter 4.7 --- Computational Studies --- p.82Chapter 4.7.1 --- Best Configuration for Path Relinking --- p.83Chapter 4.7.1.1 --- Reference Set Strategies and Initial and Guiding Criteria --- p.83Chapter 4.7.1.2 --- Frequency of Path Relinking --- p.86Chapter 4.7.1.3 --- Size of Volatile Reference Set --- p.87Chapter 4.7.1.4 --- Size of Non-volatile Reference Set --- p.89Chapter 4.7.2 --- Comparisons with Other Algorithms --- p.94Chapter 5 --- Case Study --- p.98Chapter 5.1 --- Introduction --- p.98Chapter 5.2 --- Airport Background --- p.98Chapter 5.2.1 --- Layout of ICN --- p.98Chapter 5.3 --- Data Preparation --- p.99Chapter 5.3.1 --- Passenger Data --- p.103Chapter 5.4 --- Computational Studies --- p.104Chapter 5.4.1 --- Experiments without Airline Preference --- p.104Chapter 5.4.2 --- Experiments with Airline Preference --- p.106Chapter 5.4.2.1 --- Formulation --- p.106Chapter 5.4.2.2 --- Results --- p.108Chapter 5.5 --- Conclusion --- p.111Chapter 6 --- Conclusion --- p.112Chapter 6.1 --- Summary of Achievement --- p.112Chapter 6.2 --- Future Developments --- p.113Bibliography --- p.115Appendix --- p.119Chapter 1. --- Friedman´ةs Test --- p.119Chapter 2. --- Wilcoxon's Signed Rank Test for Paired Observation --- p.120Chapter 3. --- Hybrid Simulated Annealing with Tabu Search Approach --- p.121Chapter 4. --- Arrival Flight Data of Incheon International Airport --- p.122Chapter 5. --- Departure Flight Data of Incheon International Airport --- p.13