Multi-DAT: Dynamic Job Task Scheduling Method Based on Multi-Agent Reinforcement Learning

The scheduling of aircraft support tasks requires efficient planning based on the available resources in each sup-port position. The many-to-many characteristics of tasks and the dynamic nature of scheduling environments place high demands on the real-time responsiveness of algorithms. Additionally, the complexity inherent in task scheduling and the need for flexible sequential processing further complicate decision-making. Existing methods often struggle to achieve both fast response times and effective task message capture. To address these challenges, we propose a novel scheduling method called Multi-DAT, which is designed to optimize dynamic task allocation using a multi-agent reinforcement learning algorithm. We improve on the traditional QMIX algorithm to select the shortest duration tasks based on priority, combined with a newly designed long-term reward function, which integrates long-term historical actions into the scheduling algorithm. Experimental results demonstrate that our proposed method outperforms the traditional rule-based method and seven other multi-agent reinforcement learning-based scheduling algorithms in terms of scheduling performance.