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

Beixin Xia, Yuan Li, Jiayi Gu and Yunfang Peng ()
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Beixin Xia: School of Management, Shanghai University, Shanghai 200444, China
Yuan Li: School of Management, Shanghai University, Shanghai 200444, China
Jiayi Gu: School of Management, Shanghai University, Shanghai 200444, China
Yunfang Peng: School of Management, Shanghai University, Shanghai 200444, China

Sustainability, 2024, vol. 16, issue 22, 1-15

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.

Keywords: mixed-model assembly workshop; material-handling system; Deep Q network; real-time scheduling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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