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