Airport Taxi Planning: Lagrangian Decomposition

The airport taxi planning (TP) module is a decision tool intended to guide airport surface management operations. TP is defined by a flow network optimization model that represents flight ground movements and improves aircraft taxiing routes and schedules during periods of aircraft congestion. TP is not intended to operate as a stand‐alone tool for airport operations management: on the contrary, it must be used in conjunction with existing departing and arriving traffic tools and overseen by the taxi planner of the airport, also known as the aircraft ground controller. TP must be flexible in order to accommodate changing inputs while maintaining consistent routes and schedules already delivered from past executions. Within this dynamic environment, the execution time of TP may not exceed a few minutes. Classic methods for solving binary multi‐commodity flow networks with side constraints are not efficient enough; therefore, a Lagrangian decomposition methodology has been adapted to solve it. We demonstrate TP Lagrangian decomposition using actual data from the Madrid‐Barajas Airpor

Quantum Computing for Airline Planning and Operations

Classical algorithms and mathematical optimization techniques have beenused extensively by airlines to optimize their profit and ensure that regulationsare followed. In this thesis, we explore which role quantum algorithmscan have for airlines. Specifically, we have considered the two quantum optimizationalgorithms; the Quantum Approximate Optimization Algorithm(QAOA) and Quantum Annealing (QA). We present a heuristic that integratesthese quantum algorithms into the existing classical algorithm, whichis currently employed to solve airline planning problems in a state-of-the-artcommercial solver. We perform numerical simulations of QAOA circuits andfind that linear and quadratic algorithm depth in the input size can be requiredto obtain a one-shot success probability of 0.5. Unfortunately, we areunable to find performance guarantees. Finally, we perform experiments withD-wave’s newly released QA machine and find that it outperforms 2000Q formost instances

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

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

Methods for Improving Robustness and Recovery in Aviation Planning.

In this dissertation, we develop new methods for improving robustness and recovery in aviation planning. In addition to these methods, the contributions of this dissertation include an in-depth analysis of several mathematical modeling approaches and proof of their structural equivalence. Furthermore, we analyze several decomposition approaches, the difference in their complexity and the required computation time to provide insight into selecting the most appropriate formulation for a particular problem structure. To begin, we provide an overview of the airline planning process, including the major components such as schedule planning, fleet assignment and crew planning approaches. Then, in the first part of our research, we use a recursive simulation-based approach to evaluate a flight schedule's overall robustness, i.e. its ability to withstand propagation delays. We then use this analysis as the groundwork for a new approach to improve the robustness of an airline's maintenance plan. Specifically, we improve robustness by allocating maintenance rotations to those aircraft that will most likely benefit from the assignment. To assess the effectiveness of our approach, we introduce a new metric, maintenance reachability, which measures the robustness of the rotations assigned to aircraft. Subsequently, we develop a mathematical programming approach to improve the maintenance reachability of this assignment. In the latter part of this dissertation, we transition from the planning to the recovery phase. On the day-of-operations, disruptions often take place and change aircraft rotations and their respective maintenance assignments. In recovery, we focus on creating feasible plans after such disruptions have occurred. We divide our recovery approach into two phases. In the first phase, we solve the Maintenance Recovery Problem (MRP), a computationally complex, short-term, non-recurrent recovery problem. This research lays the foundation for the second phase, in which we incorporate recurrence, i.e. the property that scheduling one maintenance event has a direct implication on the deadlines for subsequent maintenance events, into the recovery process. We recognize that scheduling the next maintenance event provides implications for all subsequent events, which further increases the problem complexity. We illustrate the effectiveness of our methods under various objective functions and mathematical programming approaches.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91539/1/mlapp_1.pd

Taxi Planner Optimization: A Management Tool

This work introduces taxi planning optimization (TPO) as a methodology to guide airport surface management operations. The optimization model represents competing aircraft using limited ground resources. TPO improves aircraft taxiing routes and their schedule in situations of congestion, minimizing overall taxiing time (TT), and helping taxi planners to meet prespecified goals such as compliance with take-off windows, TT limits, and trajectory conflicts. By considering all simultaneous trajectories during a given planning horizon, TPO's estimation of TT from the stand to the runways improves over current planning methods. The operational optimization model is a large-scale space-time multi-commodity network with capacity constraints. In addition to its natural use as a real-time taxi planning tool, a number of TPO variants can be used for design purposes, such as expansion of new infrastructure. TPO is demonstrated using Madrid-Barajas as test airport

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

Development of heuristic procedures for flight rescheduling in the aftermath of irregular airline operations

Includes bibliographical references (p. [151]-158)Airlines are constantly faced with operational problems which develop from severe weather patterns and unexpected aircraft or personnel failures. However, very little research has been done on the problem of addressing the impact of irregular operations, and developing potential decision systems which could aid in aircraft re-scheduling. The primary goal of this research project has been to develop and validate algorithms, procedures and new methodologies to be used to reschedule planned activities (flights) in the event of irregular operations in large scale scheduled transportation systems, such as airline networks. A mathematical formulation of the Airline Schedule Recovery Problem is given, along with a decision framework which is used to develop efficient solution methodologies. These heuristic procedures and algorithms have been developed for potential use in a comprehensive real-time decision support systems (DSS), incorporating several aspects of the tactical operations of the transport system. These include yield management, vehicle routing, maintenance scheduling, and crew scheduling. The heuristic procedures developed will enable the carrier to recover from an irregular operation and maintain an efficient schedule for the remainder of a given resolution horizon. The algorithms are validated using real-world operational data from a major US domestic carrier, and data from an international carrier based in the Asia Pacific region. A comprehensive case study was conducted on historical operational data to compare the output of the algorithms to what actually occurred at the airline operation control center in the aftermath of an irregularity. Some of the issues considered include the percentage of flights delayed, percentage of flights cancelled, and the overall loss in operating revenue. From these analyses, it was possible to assess the potential benefits of such algorithms on the operations of an airline