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)

Fusion of Two Metaheuristic Approaches to Solve the Flight Gate Assignment Problem

AbstractOne of the most important activity in airport operations is the gate scheduling. It is concerned with finding an assignment of flights to terminal and ramp positions (gates), and an assignment of the start and completion times of the processing of a flight at its position. The objectives related to the flight gate assignment problem (FGAP) include the minimization of the number of flights assigned to remote terminals and the minimization of the total walking distance. The main aim of this research is to find a methodology to solve the FGAP. In this paper, we propose a hybrid approach called Biogeography-based Bee Colony Optimization (B-BCO). This approach is obtained fusing two metaheuristics: biogeography-based (BBO) and bee colony optimization (BCO) algorithms. The proposed B-BCO model integrates the BBO migration operator into to bee's search behaviour. Results highlight better performances of the proposed approach in solving FGAP when compared to BCO

Non-linear integer programming fleet assignment model

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering. University of the Witwatersrand, Johannesburg, 2016Given a flight schedule with fixed departure times and cost, solving the fleet assignment problem assists airlines to find the minimum cost or maximum revenue assignment of aircraft types to flights. The result is that each flight is covered exactly once by an aircraft and the assignment can be flown using the available number of aircraft of each fleet type. This research proposes a novel, non-linear integer programming fleet assignment model which differs from the linear time-space multi-commodity network fleet assignment model which is commonly used in industry. The performance of the proposed model with respect to the amount of time it takes to create a flight schedule is measured. Similarly, the performance of the time-space multicommodity fleet assignment model is also measured. The objective function from both mathematical models is then compared and results reported. Due to the non-linearity of the proposed model, a genetic algorithm (GA) is used to find a solution. The time taken by the GA is slow. The objective function value, however, is the same as that obtained using the time-space multi-commodity network flow model. The proposed mathematical model has advantages in that the solution is easier to interpret. It also simultaneously solves fleet assignment as well as individual aircraft routing. The result may therefore aid in integrating more airline planning decisions such as maintenance routing.MT201

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

Conflict-Free Airport Operations Planning and Management

This thesis proposes conflict-free mathematical models and solution strategies for both gate scheduling and taxiway scheduling problems by taking account all meaningful airport and flight characteristics into consideration that are not yet extensively studied in current academic literature. Since gate schedule performance has a great impact on the performance of the taxiway, we consider gate scheduling as a bi-objective optimization problem, present mathematical models and propose a two-phase solution approach. We also propose a mixed integer programming (MIP) model that considers collision avoidance on the taxiways, separation distances between aircrafts, speed changes and exact travelling times without adapting a state-time network in which the decision variables are defined with time indices. Instead, the non-time segmented model proposed in this thesis, determines a taxi plan for each aircraft by identifying the sequence of taxiway intersections represented as nodes to be visited and determines the aircrafts’ exact arrival and departure times to these nodes, average speed used on the taxiway represented as links between two consecutive nodes while ensuring the safety conditions that avoid aircraft collisions. The cost incurred from arrival and departure delays with total taxiing time is minimized. The model enables collision free airport operations considering both airlines and airport controller’s objectives in continuous time where we know the exact arrival and departure times which is more accurate in tackling collision issues. However, accuracy comes with a cost of solution time. To overcome the difficulty to solve, strategies are proposed. The first strategy proposed, called the iterative-TSM, adopts a batch by batch policy and optimizes the TSM by solving it in an iterative way where in each iteration, schedules of the previous iteration are fixed. The second strategy proposed motivates from the idea of decomposition the model into two as routing and timing problem and incorporates a genetic algorithms with TSM. All the models proposed are tested on a hypothetical data and the results are presented. Main contributions of this thesis can be listed as follows: • A MILP model is presented for flight gate scheduling problem. The model is compared to modified version of one of the existing MILP model in literature and efficiency of the proposed model is evaluated. A two phase solution approach making use of the proposed MILP is also presented and the characteristics of the problem are analysed. While utilization of gates is maximized, on time performance is also considered. • A MILP that considers collision avoidance on the taxiways, separation distances between aircrafts, speed changes and exact travelling times without adapting a state-time network in which the decision variables are defined with time indices. Instead, all safety constraints are modeled with Big-Ms. This enables us to know the exact arrival and departure times for each flight on each link on the ground. • Collision free taxiway scheduling is achieved. Since the models in the existing literature either assumes arbitrary capacities on the nodes of the network or discretizes time, they do not guarantee collision avoidance. • Speed changes, rerouting, and holding at gates and taxiway intersections are used as control options. • Both airlines and airport authorities’ objectives are considered. Proposed models have the capability to be adopted as a decision support tool for the ground controllers and they allow airport traffic authorities to do what-if analysis in case of a change in the flight or airport network information. Proposed TSM also minimizes to total taxiing time which results in less costly taxiway schedules for airlines in terms of fuel costs and CO2 emissions. • Two solution strategies are proposed for the TSM: iterative TSM and GA-TSM. While iterative TSM decomposes the problem into batches of flights, solves each batch by fixing the schedules of the previous batch in each batch, GA-TSM decomposes the problem into routing and timing. While GA searches for the best set of routes for the flights, fixed TSM solves the timing problem for a given set of routes

Multi-objective genetic algorithm for robust flight scheduling

Master'sMASTER OF ENGINEERIN

Cooperation of Combinatorial Solvers for Air Traffic Management and Control

In the context of the SESAR project, Air Traffic Control (ATC) and Management (ATM) in Europe is undergoing a paradigm shift to be able to accommodate the current traffic growth forecast: many expert-based systems will be enhanced by optimization software to improve the decisionmaking process and regulation planning. Current state-of-the-art combinatorial optimization techniques that are applied to ATC and ATM include approximation algorithms like metaheuristics (e.g. Genetic Algorithm, Tabu Search, Simulated Annealing, etc.) and complete algorithms like Constraint Programming (CP) and Mixed Integer Programming. However, the large scale of the considered instances and the handling of their inherent uncertainties result in very hard problems, which can hinder or even defeat either of the previously mentioned optimization methods alone. To overcome these difficulties and improve the resolution efficiency of standard algorithms, we propose to study the generic cooperation of any set of combinatorial solvers by sharing solutions, optimization bounds and possibly other information in order to speed up the overall process. In this thesis, we have specified and implemented a distributed system which is able to integrate any combinatorial solver with the suitable interface, adapt existing solvers to take into account and provide information on the state of the search from and to other solvers, and applied this framework to two ATC and ATM problems: the en-route conflict resolution problem and the Gate Allocation Problem (GAP). For the first one, we have presented a new generic framework for the modeling and resolution of en-route conflicts in three dimensions as well as a large set of realistic instances, which have been solved with the cooperation of a Memetic Algorithm and Integer Linear Programming (ILP) solver. For the GAP, we have presented a new CP model, as well as new optimization constraints to maximize the robustness of the schedule, and search strategies together with their parallel cooperation. The solver, implemented with the FaCiLe CP library, outperforms a state-of-the-art ILP solver on real instances

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

Constructive and evolutionary algorithms for airport baggage sorting station and gate assignment problems

Correct assignment of airport resources can greatly affect the quality of service which airlines and airports provide to their customers. Good assignments can help airlines and airports to keep to published schedules, by minimising changes in these schedules and reducing delays. Given the expected increases in civil air traffic, the complexities of resource scheduling and assignment continue to increase. For this reason, as well as the dynamic nature of the problems, scheduling and assignment are becoming increasingly more difficult. The assignment of baggage sorting stations to flights is one of the resource assignment problems at an airport, and like many other real world optimisation problems, it naturally has several objectives, which conflict with each other. A model of the problem is presented, different approaches to obtaining good solutions are looked at and studied to gain an insight into their qualities. Furthermore, algorithms are studied to improve the already good solutions obtained by the approaches considered and their performance is studied where some characteristics of the problem change, such as the number of baggage sorting stations or the topology of the airport. Changes to the flight schedule on the day of operation may invalidate previous assignments of flights to resources. These perturbations may not only affect the disrupted flights but also other flights already assigned. Some existing approaches are looked at, and others are suggested to take account of these potential perturbations at the time the assignments are generated with the aim of mitigating their detrimental effect on the day of operation. The constructive search algorithms and robustness methods are potentially important in a wider variety of problems other than the Airport Baggage Sorting Station Assignment Problem (ABSSAP). By way of illustration, the same techniques are applied to the widely studied Airport Gate Assignment Problem (AGAP)