Dynamic parallel machine scheduling with random breakdowns using the learning agent

Abstract

Agent technology has been widely applied in the manufacturing process due to its flexibility, autonomy, and scalability. In this paper, the learning agent is proposed to solve a dynamic parallel machine scheduling problem which considers random breakdowns. The duty of the agent, which is based on the Q-learning algorithm, is to dynamically assign arriving jobs to idle machines according to the current state of its environment. A state-action table involving machine breakdowns is constructed to define the state of the agent's environment. Three rules, including SPT (Shortest Processing Time), EDD (Earliest Due Date) and FCFS (First Come First Served), are used as actions of the agent, and the e-greedy policy is adopted by the agent to select an action. In the simulation experiment, two different objectives, including minimising the maximum lateness and minimising the percentage of tardy jobs, are utilised to validate the ability of the learning agent. The results demonstrate that the proposed agent is suitable for the complex parallel machine environment.