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Scheduling Cluster Tools for Concurrent Processing: Deep Reinforcement Learning With Adaptive Search
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DOI：10.1109/TASE.2024.3399818.png)
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Abstract
En 中文
We address the scheduling problem of single-armed cluster tools that concurrently process two wafer types without assuming cyclic scheduling. These cluster tools, consisting of multiple processing modules and a transport robot, are commonly used in semiconductor manufacturing processes, such as etching, deposition, and lithography. To optimize the tool's throughput, we propose a reinforcement learning approach for determining both the robot task sequence and the release sequence of wafer types. By incorporating an adaptive search, our method intelligently explores future states to gather crucial information, enabling the selection of the best action. Extensive experiments demonstrate that our proposed method outperforms the well-known optimal robot task sequence for cyclic scheduling in single-armed cluster tools with concurrent processing. These findings underscore the effectiveness and superiority of our approach in optimizing the throughput of single-armed cluster tools, without relying on cyclic scheduling assumptions.
Keywords:
Job shop scheduling
Robots
Throughput
Schedules
Task analysis
Optimal scheduling
Time factors
Concurrent processing
deep reinforcement learning
Petri net
scheduling
single-armed cluster tool
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