Multi-Task Multi-Agent Reinforcement Learning for Real-Time Scheduling of a Dual-Resource Flexible Job Shop with Robots

Abstract

In this paper, a real-time scheduling problem of a dual-resource flexible job shop with robots is studied. Multiple independent robots and their supervised machine sets form their own work cells. First, a mixed integer programming model is established, which considers the scheduling problems of jobs and machines in the work cells, and of jobs between work cells, based on the process plan flexibility. Second, in order to make real-time scheduling decisions, a framework of multi-task multi-agent reinforcement learning based on centralized training and decentralized execution is proposed. Each agent interacts with the environment and completes three decision-making tasks: job sequencing, machine selection, and process planning. In the process of centralized training, the value network is used to evaluate and optimize the policy network to achieve multi-agent cooperation, and the attention mechanism is introduced into the policy network to realize information sharing among multiple tasks. In the process of decentralized execution, each agent performs multiple task decisions through local observations according to the trained policy network. Then, observation, action, and reward are designed. Rewards include global and local rewards, which are decomposed into sub-rewards corresponding to tasks. The reinforcement learning training algorithm is designed based on a double-deep Q-network. Finally, the scheduling simulation environment is derived from benchmarks, and the experimental results show the effectiveness of the proposed method.