Journal
JOURNAL OF MANUFACTURING SYSTEMS
Volume 61, Issue -, Pages 239-248Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jmsy.2021.08.008
Keywords
Scheduling; Reinforcement learning; Fuzzy; Manufacturing system
Categories
Funding
- National Natural Science Foundation of China [51905091]
- Shanghai Sailing Program [19YF1401500]
- Shanghai Science and Technology Project [20DZ2251400]
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The paper introduces a fuzzy hierarchical reinforcement learning approach for scheduling semiconductor wafer manufacturing systems to improve on-time delivery. By utilizing a hierarchical model and recurrent reinforcement learning units to address layer and wafer correlation, the control of cycle time is achieved.
Scheduling semiconductor wafer manufacturing systems has been viewed as one of the most challenging optimization problems owing to the complicated constraints, and dynamic system environment. This paper proposes a fuzzy hierarchical reinforcement learning (FHRL) approach to schedule a SWFS, which controls the cycle time (CT) of each wafer lot to improve on-time delivery by adjusting the priority of each wafer lot. To cope with the layer correlation and wafer correlation of CT due to the re-entrant process constraint, a hierarchical model is presented with a recurrent reinforcement learning (RL) unit in each layer to control the corresponding sub-CT of each integrated circuit layer. In each RL unit, a fuzzy reward calculator is designed to reduce the impact of uncertainty of expected finishing time caused by the rematching of a lot to a delivery batch. The results demonstrate that the mean deviation (MD) between the actual and expected completion time of wafer lots under the scheduling of the FHRL approach is only about 30 % of the compared methods in the whole SWFS.
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