Optimal arrival time and airspeed selection in the presence of wind uncertainty

Navigating flights through windy conditions presents a formidable challenge in the complex and interconnected realm of air transportation. The success of this endeavor hinges significantly on the meticulous utilization of wind data sourced from regularly updated hourly forecasts, which serves as the foundational bedrock for devising intricate flight routes tailored to specific aircraft trajectories. Nevertheless, despite the reliance on these meteorological predictions, ensuring timely and efficient arrivals at destination airports remains an ongoing challenge; primarily due to the inaccuracies and uncertainties inherent in such forecasts. This challenge is further compounded in scenarios involving multiple aircraft, where each flight must navigate a delicate balance between optimizing resource consumption—encompassing factors such as flight time, fuel utilization, and operational costs—and adhering to stringent scheduling requirements to minimize costs, mitigate delays, and ensure operational efficiency. To address these complex challenges in air transportation, this study adopts a comprehensive and multifaceted approach. Firstly, it aims to optimize airspeeds by carefully accounting for estimated wind uncertainty to conserve fuel while strictly meeting the Required Time of Arrival (RTA) for individual aircraft operations. This complex optimization process integrates core principles from both Stochastic Programming (SP) and Receding Horizon Control (RHC) schemes, enabling the determination of the optimal airspeed selection for each segment or the flight route. The study introduces two innovative models: the model with "k-segment look-ahead", designed to optimize airspeeds during the cruise phase, and the "backward time boundaries propagation" model, crafted to optimize airspeeds during the cruise and descent phases. Both models are meticulously developed using the branch and bound method, ensuring resilience, accuracy, and efficiency in optimizing airspeeds to navigate challenging wind conditions. Furthermore, we created two baseline models that disregard wind forecast uncertainty. The proposed models achieve fuel savings of up to 3.5% and 4.3% over the baseline models. Additionally, time deviation from the RTA is reduced by up to 15 seconds. Furthermore, in its pursuit of fairness and equitable treatment among flights, the study introduces the innovative concept of Cost Index (CI) to quantify resource usage. CI is a detailed, monetized metric that combines fuel consumption and flight time into one measurement. By employing CI, the study seeks to optimize resource allocation across multiple aircraft, fostering fairness, equity, and efficiency in air traffic management. This endeavor involves dynamically adjusting RTAs across flights to minimize a designated fairness metric value while maintaining fairness and equity principles. The resolution of this complex problem is achieved through the advanced application of integer programming techniques, combined with a sophisticated branch and bound optimization method. A model with fixed RTAs, which does not consider fairness, is developed for comparison. Through numerical experiments, the proposed model demonstrates improved fairness, more equitable fuel consumption rate changes, and better RTA adjustments for individual flights compared to the fixed RTA model. In summary, the study endeavors to comprehensively address the myriad of challenges posed by windy conditions in air transportation through a multifaceted, innovative, and exhaustive approach. By integrating the concept of CI and leveraging cutting-edge optimization methodologies, the study aims not only to optimize resource utilization but also to promote fairness, enhance efficiency, and ultimately drive forward the comprehensive progression of the aviation industry. This comprehensive approach, rooted in thorough research and advanced optimization methods, promises to transform air traffic management, ushering in a new era of efficiency, fairness, and sustainability in air transportation.

Name thesis:degree_name

Doctor of Philosophy

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

The University of Texas at Austin

Year dc:date.issued

2024

Author dc:creator

• Jung, Hyunho

Advisors dc:contributor.advisor

• Clarke, John-Paul
• Wang, Junmin, 1974-

Committee member dc:contributor.committeemember

• Raul Longoria

• Stochastic programming
• Air traffic management
• Receding horizon control
• Optimal airspeed search
• Fairness

Identifier URI

https://doi.org/10.26153/tsw/56634

OAI identifier oai:identifier

oai:repositories.lib.utexas.edu:2152/130135