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

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

Planning and reconfigurable control of a fleet of unmanned vehicles for taxi operations in airport environment

The optimization of airport operations has gained increasing interest by the aeronautical community, due to the substantial growth in the number of airport movements (landings and take-offs) experienced in the past decades all over the world. Forecasts have confirmed this trend also for the next decades. The result of the expansion of air traffic is an increasing congestion of airports, especially in taxiways and runways, leading to additional amount of fuel burnt by airplanes during taxi operations, causing additional pollution and costs for airlines. In order to reduce the impact of taxi operations, different solutions have been proposed in literature; the solution which this dissertation refers to uses autonomous electric vehicles to tow airplanes between parking lots and runways. Although several analyses have been proposed in literature, showing the feasibility and the effectiveness of this approach in reducing the environmental impact, at the beginning of the doctoral activity no solutions were proposed, on how to manage the fleet of unmanned vehicles inside the airport environment. Therefore, the research activity has focused on the development of algorithms able to provide pushback tractor (also referred as tugs) autopilots with conflict-free schedules. The main objective of the optimization algorithms is to minimize the tug energy consumption, while performing just-in-time runway operations: departing airplanes are delivered only when they can take-off and the taxi-in phase starts as soon as the aircraft clears the runway and connects to the tractor. Two models, one based on continuous time and one on discrete time evolution, were developed to simulate the taxi phases within the optimization scheme. A piecewise-linear model has also been proposed to evaluate the energy consumed by the tugs during the assigned missions. Furthermore, three optimization algorithms were developed: two hybrid versions of the particle swarm optimization and a tree search heuristic. The following functional requirements for the management algorithm were defined: the optimization model must be easily adapted to different airports with different layout (reconfigurability); the generated schedule must always be conflict-free; and the computational time required to process a time horizon of 1h must be less than 15min. In order to improve its performance, the particle swarm optimization was hybridized with a hill-climb meta-heuristic; a second hybridization was performed by means of the random variable search, an algorithm of the family of the variable neighborhood search. The neighborhood size for the random variable search was considered varying with inverse proportionality to the distance between the actual considered solution and the optimal one found so far. Finally, a tree search heuristic was developed to find the runway sequence, among all the possible sequences of take-offs and landings for a given flight schedule, which can be realized with a series of taxi trajectories that require minimum energy consumption. Given the taxi schedule generated by the aforementioned optimization algorithms a tug dispatch algorithm, assigns a vehicle to each mission. The three optimization schemes and the two mathematical models were tested on several test cases among three airports: the Turin-Caselle airport, the Milan-Malpensa airport, and the Amsterdam airport Schiphol. The cost required to perform the generated schedules using the autonomous tugs was compared to the cost required to perform the taxi using the aircraft engines. The proposed approach resulted always more convenient than the classical one

A Hybrid Tabu/Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling

As air traffic continues to increase, air traffic flow management is becoming more challenging to effectively and efficiently utilize airport capacity without compromising safety, environmental and economic requirements. Since runways are often the primary limiting factor in airport capacity, runway operations scheduling emerge as an important problem to be solved to alleviate flight delays and air traffic congestion while reducing unnecessary fuel consumption and negative environmental impacts. However, even a moderately sized real-life runway operations scheduling problem tends to be too complex to be solved by analytical methods, where all mathematical models for this problem belong to the complexity class of NP-Hard in a strong sense due to combinatorial nature of the problem. Therefore, it is only possible to solve practical runway operations scheduling problem by making a large number of simplifications and assumptions in a deterministic context. As a result, most analytical models proposed in the literature suffer from too much abstraction, avoid uncertainties and, in turn, have little applicability in practice. On the other hand, simulation-based methods have the capability to characterize complex and stochastic real-life runway operations in detail, and to cope with several constraints and stakeholders’ preferences, which are commonly considered as important factors in practice. This dissertation proposes a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling problem. The SbO approach utilizes a discrete-event simulation model for accounting for uncertain conditions, and an optimization component for finding the best known Pareto set of solutions. This approach explicitly considers uncertainty to decrease the real operational cost of the runway operations as well as fairness among aircraft as part of the optimization process. Due to the problem’s large, complex and unstructured search space, a hybrid Tabu/Scatter Search algorithm is developed to find solutions by using an elitist strategy to preserve non-dominated solutions, a dynamic update mechanism to produce high-quality solutions and a rebuilding strategy to promote solution diversity. The proposed algorithm is applied to bi-objective (i.e., maximizing runway utilization and fairness) runway operations schedule optimization as the optimization component of the SbO framework, where the developed simulation model acts as an external function evaluator. To the best of our knowledge, this is the first SbO approach that explicitly considers uncertainties in the development of schedules for runway operations as well as considers fairness as a secondary objective. In addition, computational experiments are conducted using real-life datasets for a major US airport to demonstrate that the proposed approach is effective and computationally tractable in a practical sense. In the experimental design, statistical design of experiments method is employed to analyze the impacts of parameters on the simulation as well as on the optimization component’s performance, and to identify the appropriate parameter levels. The results show that the implementation of the proposed SbO approach provides operational benefits when compared to First-Come-First-Served (FCFS) and deterministic approaches without compromising schedule fairness. It is also shown that proposed algorithm is capable of generating a set of solutions that represent the inherent trade-offs between the objectives that are considered. The proposed decision-making algorithm might be used as part of decision support tools to aid air traffic controllers in solving the real-life runway operations scheduling problem

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

Stochastic airport gate assignment problem

The uncertainties inherent in the airport flight arrival and departure traffic may lead to the unavailability of gates when needed to accommodate scheduled flights. Mechanical failures, severe weather conditions, heavy traffic volume at the airport are some typical causes of the uncertainties in the input data. Incorporating such random disruptions is crucial in constructing effective flight-gate assignment plans. We consider the flight-gate assignment problem in the presence of uncertainty in arrival and departure times of the flights and represent the randomness associated with these uncertain parameters by a finite set of scenarios. Using the scenario-based approach, we develop new stochastic programming models incorporating alternate robustness measures to obtain assignments that would perform well under potential random disruptions. In particular, we focus on the number of confficting flights, the buffer and idle times as robustness measures. Minimizing the expected variance of idle times or the expected semi-deviation of idle times from a buffer time value are some examples of the objectives that we incorporate in our models to appropriately distribute the idle times among gates, and by this way, to decrease the number of potential flight confficts. The proposed stochastic optimization models are formulated as computationally expensive large-scale mixed-integer programming problems, which are hard to solve. In order to find good feasible solutions in reasonably short CPU times, we employ tabu search algorithms. We conduct an extensive computational study to analyze the proposed alternate formulations and show the computational effectiveness of the proposed solution methods

Robust assignment of airport gates with operational safety constraints

This paper reviews existing approaches to the airport gate assignment problem (AGAP) and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods (to get robust optimization) while ensuring appropriate matching between the size of each aircraft and its assigned gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem. An illustrative example is given to show the effectiveness and efficiency of the algorithm. The algorithm performance is further demonstrated using data of a terminal from Beijing Capital International Airport (PEK)

Airport gate assignment with airline preferences

Das airport gate assignment problem gewann in den letzten Jahrzehnten aufgrund der steigenden Dichte des Flugverkehrs und den daraus resultierenden dichter werdenden Flugplänen der Fluglinien immer mehr an Bedeutung. Das in dieser Magisterarbeit vorgestellte mathematische Modell konzentriert sich einerseits auf eine gute/bestmögliche Erfüllung der Präferenzen der Fluglinien für die gewünschten Parkpositionen, sowie andererseits auf die Erstellung einer Abstellzuordnung die so unanfällig wie möglich in Bezug auf Flugverspätungen ist. Die implementierte Metaheuristik wurde zuerst auf einige kleinere Testinstanzen und in weiterer Folge auf eine große Testinstanz, die einen 24 Stunden Flugbetrieb des Flughafen Wiens widerspiegelt, angewendet. Verglichen mit einem exakten Verfahren liefert die Metaheuristik, bei einem Bruchteil der benötigten Rechenzeit, sehr gute (teilweise auch bessere) Ergebnisse.The airport gate assignment problem is an important part of airport oriented research and gained more and more in importance during the last decades due to growing air traffic and subsequent tighter airline schedules. The new deterministic model presented in this thesis focuses on the satisfaction of airline preferences as well as on the generation of a robust schedule which aims to be as insusceptible as possible against aircraft delays. First applied to small instances derived from real-world data and then applied to a large instance containing a 24-hour traffic progression at Vienna International Airport, the implemented metaheuristic Large Neighborhood Search showed very good results regarding runtime and solution quality compared to benchmarks created by a mixed integer program solver

Optimal Reassignment of Flights to Gates Focusing on Transfer Passengers

This dissertation focuses on the optimal flight-to-gate assignment in cases of schedule disruptions with a focus on transfer passengers. Disruptions result from increased passenger demand, combined with tight scheduling and limited infrastructure capacity. The critical role of gate assignment, combined with the scarcity of models and algorithms to handle passenger connections, is the main motivation for this study. Our first task is to develop a generalizable multidimensional assignment model that considers the location of gates and the required connection time to assess the success of passenger transfers. The results demonstrate that considering gate location is critical for assessing of the success of a connection, since transfer passengers contribute significantly to total cost. We then explore the mathematical programming formulation of the problem. First, we compare different state-of-art mathematical formulations, and identify their underlying assumptions. Then, we strengthen our time-index formulation by introducing valid inequalities. Afterwards, we express the cost of passenger connections using an aggregating formulation, which outperforms the quadratic formulation and is consistently more efficient than network flow formulations when the cost of successful connections is considered. In the last part of the dissertation, we embed the formulation in an MIP-based metaheuristic framework using Variable Neighborhood Search with Local Branching (VNS-LB). We explore the key notion of a solution neighborhood in the context of gate assignment, given that transfer passengers are our main consideration. Our implementation produces near-optimal results in a low amount of time and responds reasonably to sensitivity analysis in operating parameters and external conditions. Furthermore, VNS-LB is shown to outperform the Local Branching heuristic in terms of solution quality. Finally, we propose a set of extensions to the algorithm which are shown to improve the quality of the final solution, as well as the progress of the optimization procedure as a whole. This dissertation aspires to develop a versatile tool that can be adapted to the objectives and priorities of practitioners, and to provide researchers with an insight of how the features of a solution are reflected in the mathematical formulation. Every idea relying on these principles should be a promising path for future research