A Hybrid Tabu/Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling

2025-02-08

AI Summary

This study addresses the challenge of jointly optimizing airport capacity utilization, flight delay, fuel consumption, and environmental impact in multi-objective runway scheduling. Method: We propose a simulation-driven robust multi-objective optimization framework. It introduces a novel hybrid metaheuristic—integrating Tabu Search and Scatter Search—and explicitly incorporates flight fairness into Pareto-optimal multi-objective optimization. Flight stochasticity and dynamic uncertainties are modeled via discrete-event simulation. Contribution/Results: Evaluated on real-world data from a major U.S. airport, the framework achieves significant improvements: average delay reduced by 23.6%, fuel consumption by 15.2%, and fairness enhanced (Gini coefficient improved by 0.18). It consistently outperforms First-Come-First-Served (FCFS) and conventional deterministic approaches across all objectives, demonstrating strong potential for real-time decision support in air traffic management.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Optimize multi-objective runway scheduling
• Handle real-world uncertainties in air traffic
• Minimize delays and environmental impacts

Innovation

• Hybrid Tabu-Scatter Search algorithm
• Simulation-based optimization framework
• Discrete-event simulation model