A Deep Reinforcement Learning Framework for Multi-Fleet Scheduling and Optimization of Hybrid Ground Support Equipment Vehicles in Airport Operations

Abstract

The increasing electrification of Ground Support Equipment (GSE) vehicles promotes sustainable airport operations but introduces new challenges in task scheduling, energy management, and hybrid fleet coordination. To address these issues, we develop an end-to-end Deep Reinforcement Learning (DRL) framework and evaluate it under three representative deployment scenarios with 30%, 50%, and 80% electric fleet proportions through case studies at Singapore’s Changi Airport. Experimental results show that the proposed approach outperforms baseline models, achieves more balanced state-of-charge (SoC) distributions, reduces overall carbon emissions, and improves real-time responsiveness under operational constraints. Beyond these results, this work contributes a unified DRL-based scheduling paradigm that integrates electric and fuel-powered vehicles, adapts Proximal Policy Optimization (PPO) to heterogeneous fleet compositions, and provides interpretable insights through Gantt chart visualizations. These findings demonstrate the potential of DRL as a scalable and robust solution for smart airport logistics.