Problem
Research questions and friction points this paper is trying to address.
Dynamic shop floor scheduling
Manufacturing uncertainty
RFID-enabled smart factory
Production scheduling
A Shop Floor Production Scheduling Case based on RFID-supported Smart Factory
🤖 AI Summary
This study addresses dynamic scheduling challenges arising from operational and production uncertainties in RFID-enabled smart factories by proposing a novel framework integrating data mining with deep reinforcement learning. The approach extracts feasible sequences from RFID data and estimates real-time processing rates to precisely quantify manufacturing uncertainties, thereby guiding reinforcement learning policy optimization. Simulation experiments demonstrate that the proposed framework significantly outperforms baseline methods, including FIFO, LIFO, and conventional DQN, in minimizing makespan. These results confirm its effectiveness in enhancing both robustness and efficiency for shop-floor scheduling under uncertain conditions. Ultimately, this work establishes a new paradigm for data-driven intelligent production scheduling in complex manufacturing environments.
📝 Abstract
Radio frequency identification (RFID) technology has been widely implemented for real-time data collection in manufacturing shop floors, which, in turn, can be used to support dynamic shop floor production planning and scheduling. Within such an environment, uncertainty in operation and production processes collectively contribute to the dynamicity in manufacturing, thereby hampering the scheduling system from achieving maximal utility. To highlight the importance of handling such uncertainty, this paper addresses the problem of dynamic shop floor scheduling for a real-life case smart factory equipped with RFID technology. Feasible production sequence mining and real-time processing rate estimation are conducted on RFID-collected production data to quantify the operation and production uncertainties. A deep reinforcement learning approach based on the RFID data analysis is then presented for shop floor production scheduling. Simulation studies based on real-life case data have demonstrated the feasibility and practicality of the proposed dynamic production scheduling framework. Specifically, it is observed that the proposed framework outperforms existing dispatch methods in terms of minimizing operation makespan, including first in first out (FIFO), last in first out (LIFO) and deep Q network (DQN).
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep Reinforcement Learning
RFID
Dynamic Scheduling
Uncertainty Quantification
Smart Factory
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