A Hybrid Tabu Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling
- URL: http://arxiv.org/abs/2502.05594v1
- Date: Sat, 08 Feb 2025 14:42:05 GMT
- Title: A Hybrid Tabu Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling
- Authors: Bulent Soykan,
- Abstract summary: dissertation addresses the challenge of air traffic flow management by proposing a simulation-based optimization (SbO) approach for runway operations scheduling.
The goal is to optimize airport capacity utilization while minimizing delays, fuel consumption, and environmental impacts.
The proposed SbO framework integrates a discrete-event simulation model to handle runway conditions and a hybrid Tabu-Scatter Search algorithm to identify optimal solutions.
- Score: 0.0
- License:
- Abstract: This dissertation addresses the growing challenge of air traffic flow management by proposing a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling. The goal is to optimize airport capacity utilization while minimizing delays, fuel consumption, and environmental impacts. Given the NP-Hard complexity of the problem, traditional analytical methods often rely on oversimplifications and fail to account for real-world uncertainties, limiting their practical applicability. The proposed SbO framework integrates a discrete-event simulation model to handle stochastic conditions and a hybrid Tabu-Scatter Search algorithm to identify Pareto-optimal solutions, explicitly incorporating uncertainty and fairness among aircraft as key objectives. Computational experiments using real-world data from a major U.S. airport demonstrate the approach's effectiveness and tractability, outperforming traditional methods such as First-Come-First-Served (FCFS) and deterministic approaches while maintaining schedule fairness. The algorithm's ability to generate trade-off solutions between competing objectives makes it a promising decision support tool for air traffic controllers managing complex runway operations.
Related papers
- Over-the-Air Fair Federated Learning via Multi-Objective Optimization [52.295563400314094]
We propose an over-the-air fair federated learning algorithm (OTA-FFL) to train fair FL models.
Experiments demonstrate the superiority of OTA-FFL in achieving fairness and robust performance.
arXiv Detail & Related papers (2025-01-06T21:16:51Z) - Optimization-Driven Adaptive Experimentation [7.948144726705323]
Real-world experiments involve batched & delayed feedback, non-stationarity, multiple objectives & constraints, and (often some) personalization.
Tailoring adaptive methods to address these challenges on a per-problem basis is infeasible, and static designs remain the de facto standard.
We present a mathematical programming formulation that can flexibly incorporate a wide range of objectives, constraints, and statistical procedures.
arXiv Detail & Related papers (2024-08-08T16:29:09Z) - A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility [5.19664437943693]
This paper presents a comprehensive optimization formulation of the fleet scheduling problem.
It also identifies the need for alternate solution approaches.
The new imitative approach achieves better mean performance and remarkable improvement in the case of unseen worst-case scenarios.
arXiv Detail & Related papers (2024-07-16T18:51:24Z) - SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics [13.129654942805846]
Model Predictive Control (MP)-based trajectory planning has been widely used in, and Control Barrier (CBF) can improve its constraints.
In this paper, we propose a self-supervised learning algorithm for CBF-MPC trajectory planning.
arXiv Detail & Related papers (2024-05-15T09:38:52Z) - OTClean: Data Cleaning for Conditional Independence Violations using
Optimal Transport [51.6416022358349]
sys is a framework that harnesses optimal transport theory for data repair under Conditional Independence (CI) constraints.
We develop an iterative algorithm inspired by Sinkhorn's matrix scaling algorithm, which efficiently addresses high-dimensional and large-scale data.
arXiv Detail & Related papers (2024-03-04T18:23:55Z) - Airport take-off and landing optimization through genetic algorithms [55.2480439325792]
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.
arXiv Detail & Related papers (2024-02-29T14:53:55Z) - Federated Conditional Stochastic Optimization [110.513884892319]
Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML.
This paper proposes algorithms for distributed federated learning.
arXiv Detail & Related papers (2023-10-04T01:47:37Z) - Stochastic Methods for AUC Optimization subject to AUC-based Fairness
Constraints [51.12047280149546]
A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints.
We formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints.
We demonstrate the effectiveness of our approach on real-world data under different fairness metrics.
arXiv Detail & Related papers (2022-12-23T22:29:08Z) - Robust Constrained Multi-objective Evolutionary Algorithm based on
Polynomial Chaos Expansion for Trajectory Optimization [0.0]
The proposed method rewrites a robust formulation into a deterministic problem via the PCE.
As a case study, the landing trajectory design of supersonic transport (SST) with wind uncertainty is optimized.
arXiv Detail & Related papers (2022-05-23T15:33:05Z) - 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)
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.