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In response to the challenge of Master Production Scheduling (MPS) for Prefabricated Components (PC) manufacturers, this study proposes an MPS approach utilizing the Q-learning algorithm and develops a mathematical model for MPS in PC manufacturing. The state set is equationted based on the weekly production task load, the action set is defined by weekly production orders, and rewards are contingent upon the delivery time of customer orders. This method significantly enhances MPS efficiency in PC manufacturing operations and optimally utilizes production capacity.

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Research on Master Production Scheduling for Prefabricated Components Manufacturers

  • 会议论文

  • 发表日期：

    2025年

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  • Tianhui Zhang

摘要

In response to the challenge of Master Production Scheduling (MPS) for Prefabricated Components (PC) manufacturers, this study proposes an MPS approach utilizing the Q-learning algorithm and develops a mathematical model for MPS in PC manufacturing. The state set is equationted based on the weekly production task load, the action set is defined by weekly production orders, and rewards are contingent upon the delivery time of customer orders. This method significantly enhances MPS efficiency in PC manufacturing operations and optimally utilizes production capacity.