Deep Reinforcement Learning Optimizes Smart Factory Scheduling with RFID Data

This paper introduces a novel dynamic shop floor production scheduling framework for smart factories that utilize RFID technology. The approach addresses uncertainties in manufacturing processes by analyzing RFID-collected data to estimate production uncertainties and mining feasible production sequences. A deep reinforcement learning method, specifically a Deep Q-Network (DQN), is employed to optimize scheduling, outperforming traditional methods like FIFO and LIFO in minimizing makespan. AI

IMPACT This research could lead to more efficient manufacturing operations by improving production scheduling in smart factories.

RANK_REASON The item is an academic paper detailing a new methodology for production scheduling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Deep Q-Network
• deep reinforcement learning
• FIFO
• LiFO
• radio-frequency identification
• Smart Factory Tycoon

• paper
• infra

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