A Multiagent Transformer-Based Algorithm for Multitask Dynamic Scheduling with Constrained Machines

Modern manufacturers often require handling multiple tasks simultaneously under dynamic environments by sharing constrained machines. Existing multitask scheduling algorithms typically focus on transferring knowledge among multiple tasks. However, these algorithms overlook the need for collaborative multitasking efforts required to share constrained machines. To overcome this limitation, we propose a multiagent transformer (MAT)-based algorithm to solve multitask dynamic scheduling with constrained machines. Specifically, we first formulate the multitask scheduling problem as a sequential multiagent decision-making process, enabling agents to make collaborative decisions by accessing the actions of others. Furthermore, a joint policy network is developed to support the agents in adaptively selecting the appropriate heuristic for each task. It improves the decision-making quality by enabling agents to leverage common and task-specific knowledge. In addition, a comprehensive reward function is designed to guide the learning of a joint policy network for collaborative decision-making across tasks. This ensures that agents holistically consider the objectives of all tasks during the learning process. With these designs, the proposed algorithm can effectively address multiple tasks through collaborative machine sharing. The proposed algorithm is evaluated against 14 state-of-the-art competitors on 270 instances with varying scales. The results confirm that the proposed algorithm outperforms all competitors on each instance. In addition, the ablation study demonstrates the effectiveness of distinct reward mechanisms, revealing that the joint policy network makes more informed decisions by leveraging both individual and common knowledge.