Airport take-off and landing optimization through genetic algorithms
- URL: http://arxiv.org/abs/2402.19222v1
- Date: Thu, 29 Feb 2024 14:53:55 GMT
- Title: Airport take-off and landing optimization through genetic algorithms
- Authors: Fernando Guedan Pecker and Cristian Ramirez Atencia
- Abstract summary: This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously.
The study presents an innovative genetic algorithm-based method for minimizing pollution from fuel combustion during aircraft take-off and landing at airports.
- Score: 55.2480439325792
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This research addresses the crucial issue of pollution from aircraft
operations, focusing on optimizing both gate allocation and runway scheduling
simultaneously, a novel approach not previously explored. The study presents an
innovative genetic algorithm-based method for minimizing pollution from fuel
combustion during aircraft take-off and landing at airports. This algorithm
uniquely integrates the optimization of both landing gates and take-off/landing
runways, considering the correlation between engine operation time and
pollutant levels. The approach employs advanced constraint handling techniques
to manage the intricate time and resource limitations inherent in airport
operations. Additionally, the study conducts a thorough sensitivity analysis of
the model, with a particular emphasis on the mutation factor and the type of
penalty function, to fine-tune the optimization process. This dual-focus
optimization strategy represents a significant advancement in reducing
environmental impact in the aviation sector, establishing a new standard for
comprehensive and efficient airport operation management.
Related papers
- Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation [49.49868273653921]
Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving.
We introduce Optimal Gaussian Diffusion (OGD) and Estimated Clean Manifold (ECM) Guidance.
Our methodology streamlines the generative process, enabling practical applications with reduced computational overhead.
arXiv Detail & Related papers (2024-08-01T17:59:59Z) - Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm [26.061782031525652]
Weather disaster related emergency operations pose a great challenge to air mobility in both aircraft and airport operations.
We propose an optimized framework for adjusting airport operational schedules for such pre-disaster scenarios.
We then propose a novel Neural Network (NN) accelerated Genetic Algorithm(GA) for evacuation planning.
arXiv Detail & Related papers (2024-07-17T15:59:41Z) - UAS-based Automated Structural Inspection Path Planning via Visual Data
Analytics and Optimization [1.1496057626375067]
Unmanned Aerial Systems (UAS) have gained significant traction for their application in infrastructure inspections.
One of the core problems in this regard is electing an optimal automated flight path.
This paper presents an effective formulation for the path planning problem in the context of structural inspections.
arXiv Detail & Related papers (2023-12-22T23:07:20Z) - Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via
Optimization Trajectory Distillation [73.83178465971552]
The success of automated medical image analysis depends on large-scale and expert-annotated training sets.
Unsupervised domain adaptation (UDA) has been raised as a promising approach to alleviate the burden of labeled data collection.
We propose optimization trajectory distillation, a unified approach to address the two technical challenges from a new perspective.
arXiv Detail & Related papers (2023-07-27T08:58:05Z) - A Simplified Framework for Air Route Clustering Based on ADS-B Data [0.0]
This paper presents a framework that can support to detect the typical air routes between airports based on ADS-B data.
As a matter of fact, our framework can be taken into account to reduce practically the computational cost for air flow optimization.
arXiv Detail & Related papers (2021-07-07T08:55:31Z) - Reinforcement Learning for Low-Thrust Trajectory Design of
Interplanetary Missions [77.34726150561087]
This paper investigates the use of reinforcement learning for the robust design of interplanetary trajectories in presence of severe disturbances.
An open-source implementation of the state-of-the-art algorithm Proximal Policy Optimization is adopted.
The resulting Guidance and Control Network provides both a robust nominal trajectory and the associated closed-loop guidance law.
arXiv Detail & Related papers (2020-08-19T15:22:15Z) - Combining Deep Learning and Optimization for Security-Constrained
Optimal Power Flow [94.24763814458686]
Security-constrained optimal power flow (SCOPF) is fundamental in power systems.
Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs.
This paper proposes a novel approach that combines deep learning and robust optimization techniques.
arXiv Detail & Related papers (2020-07-14T12:38:21Z) - A Hybrid Multi-Objective Carpool Route Optimization Technique using
Genetic Algorithm and A* Algorithm [0.0]
This work presents a hybrid GA-A* algorithm to obtain optimal routes for the carpooling problem.
The routes obtained maximize the profit of the service provider by minimizing the travel and detour distance as well as pick-up/drop costs.
The proposed algorithm has been implemented over the Salt Lake area of Kolkata.
arXiv Detail & Related papers (2020-07-11T14:13:20Z) - Congestion-aware Evacuation Routing using Augmented Reality Devices [96.68280427555808]
We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations.
A population density map, obtained on-the-fly by aggregating locations of evacuees from user-end Augmented Reality (AR) devices, is used to model the congestion distribution inside a building.
arXiv Detail & Related papers (2020-04-25T22:54:35Z) - A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air
Traffic Control [5.550794444001022]
We propose a new intelligent decision making framework that leverages multi-agent reinforcement learning (MARL) to suggest adjustments of aircraft speeds in real-time.
The goal of the system is to enhance the ability of an air traffic controller to provide effective guidance to aircraft to avoid air traffic congestion, near-miss situations, and to improve arrival timeliness.
arXiv Detail & Related papers (2020-04-03T06:03:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.