Dynamic parallel machine scheduling using the learning agent

Static and dynamic machine scheduling problems have been widely addressed in literature. Compared with static scheduling, dynamic scheduling is more difficult since the detailed information about jobs and machines (like the arrival time of jobs) is not available at the initial time. Hence the lack of information makes dynamic scheduling problems harder than static ones. In this paper, the learning agent based scheduling system is developed to dynamically schedule machines in parallel. The scheduling system contains the learning agent and the system environment. The agent is trained by the Q-Learning algorithm, and the best rule is selected according to the current state of the system, while the system environment executes the rule selected by the agent. In the simulation experiment, the proposed agent uses the rules of SPT, EDD and FCFS as actions, and is tested with two objectives: minimizing the maximum lateness and minimizing percentage of tardy jobs. The results demonstrate that the learning agent is suitable for complex dynamic parallel machine scheduling.