Research on Sustainable Scheduling of Material-Handling Systems in Mixed-Model Assembly Workshops Based on Deep Reinforcement Learning

Abstract

In order to dynamically respond to changes in the state of the assembly line and effectively balance the production efficiency and energy consumption of mixed-model assembly, this paper proposes a deep reinforcement learning sustainable scheduling model based on the Deep Q network. According to the particularity of the workshop material-handling system, the action strategy and reward and punishment function are designed, and the neural network structure, parameter update method, and experience pool selection method of the original Deep Q network dual neural network are improved. Prioritized experience replay is adopted to form a real-time scheduling method for workshop material handling based on the Prioritized Experience Replay Deep Q network. The simulation results demonstrate that compared with other scheduling methods, this deep reinforcement learning approach significantly optimizes material-handling scheduling in mixed-flow assembly workshops, effectively reducing handling distance while ensuring timely delivery to the assembly line, ultimately achieving maximum output with sustainable considerations.