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
- Abstract summary: dissertation addresses the challenge of air traffic flow management by proposing a simulation-based optimization (SbO) approach for runway operations scheduling.<n>The goal is to optimize airport capacity utilization while minimizing delays, fuel consumption, and environmental impacts.<n>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: http://creativecommons.org/publicdomain/zero/1.0/
- 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
- Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling [50.176885352114674]
Heterogeneous agile Earth observation satellite (AEOS) scheduling requires task selection, satellite assignment, and observation sequencing.<n>This paper proposes an evolutionary policy optimization framework for heterogeneous AEOS scheduling with preference-adjustable weighted objectives.
arXiv Detail & Related papers (2026-08-25T12:16:28Z) - ASPaeroFlow: Decomposition Heuristics for Joint Air Traffic Flow & Capacity Management [60.85136195443409]
We propose ASPaeroFlow: a for the joint Air Traffic Flow and Capacity Management (ATFCM)<n>It combines instance-space decompositions with a local exact approach using Answer Set Programming.<n>We benchmark ASPaeroFlow from small to industry-sized instances and compare it with exact and alternative approaches.
arXiv Detail & Related papers (2026-08-10T08:57:18Z) - Adaptive decision-making for stochastic service network design [1.2154569665167423]
This paper addresses the Service Network Design (SND) problem for a logistics service provider (LSP) operating in a multimodal freight transport network.<n>A two-stage optimization approach is proposed, which combines metaheuristics, simulation and machine learning components.
arXiv Detail & Related papers (2026-03-25T14:51:57Z) - MMH-Planner: Multi-Mode Hybrid Trajectory Planning Method for UAV Efficient Flight Based on Real-Time Spatial Awareness [5.419189180406189]
This paper proposes a multi-mode hybrid trajectory planning method for UAVs based on real-time environmental awareness.<n>We introduce a goal-oriented spatial awareness method that rapidly assesses flight safety in the upcoming environments.<n>Second, a multi-mode hybrid trajectory planning mechanism is proposed, which can enhance the planning efficiency.
arXiv Detail & Related papers (2026-03-03T07:23:19Z) - Adaptive Linear Path Model-Based Diffusion [52.84663832658799]
We introduce Linear Path Model-Based Diffusion (LP-MBD), which replaces the variance-preserving schedule with a flow-matching linear probability path.<n>We also propose Adaptive LP-MBD (ALP-MBD), which leverages reinforcement learning to adjust diffusion steps and noise levels according to task complexity and environmental conditions.
arXiv Detail & Related papers (2026-02-02T21:33:03Z) - A Fast Semidefinite Convex Relaxation for Optimal Control Problems With Spatio-Temporal Constraints [22.24436694865408]
Solving optimal control problems (OCs) of autonomous agents is essential in applications ranging from driving autonomous vehicles to self-driving cars.<n>Most of the OCPs are nonpointing due to the coupling between the event timing and the trajectory.<n>We propose a time-scaling direct multiple shooting scheme that partitions the horizon into segments with time constraints.
arXiv Detail & Related papers (2026-01-06T14:38:19Z) - An Experimental Approach for Running-Time Estimation of Multi-objective Evolutionary Algorithms in Numerical Optimization [16.66619776655723]
We propose an experimental approach for estimating upper bounds on the running time of MOEAs without algorithmic assumptions.<n>We conduct comprehensive experiments on five representative MOEAs using the ZDT and DTLZ benchmark suites.<n>Results demonstrate the effectiveness of our approach in estimating upper bounds on the running time without requiring algorithmic or problem simplifications.
arXiv Detail & Related papers (2025-07-03T07:06:14Z) - Online Decision-Focused Learning [63.83903681295497]
Decision-focused learning (DFL) is an increasingly popular paradigm for training predictive models whose outputs are used in decision-making tasks.<n>We investigate DFL in dynamic environments where the objective function does not evolve over time.<n>We establish bounds on the expected dynamic regret, both when decision space is a simplex and when it is a general bounded convex polytope.
arXiv Detail & Related papers (2025-05-19T10:40:30Z) - Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
arXiv Detail & Related papers (2025-05-13T16:47:00Z) - 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) - A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system.
We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks.
arXiv Detail & Related papers (2024-08-29T06:57:45Z) - 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) - DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimization [9.28162057044835]
Optimal trajectory local is computationally expensive for nonlinear and high-dimensional dynamical systems.
In this paper we introduce Diffu-based general model for non-dimensional optima problems.
We also present Diff+, a novel constrained diffusion model with an additional loss in that further reduces the problem violations.
arXiv Detail & Related papers (2024-02-22T03:52:17Z) - 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) - Maximize to Explore: One Objective Function Fusing Estimation, Planning,
and Exploration [87.53543137162488]
We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX.
textttMEX integrates estimation and planning components while balancing exploration exploitation automatically.
It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
arXiv Detail & Related papers (2023-05-29T17:25:26Z) - 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.