Idle Vehicle Rebalancing in Semiconductor Fabrication Using Factorized Graph Neural Network Reinforcement Learning

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With the advancement in the semiconductor industry, the size of fab becomes larger and thus more overhead hoist transportation (OHT) vehicles need to be operated, which necessitate efficient operation strategies for a large number of OHTs. In this study, we propose a cooperative rebalancing strategy of OHTs to increase the overall productivity of the material handling process in the fab. We discretize the fab into a number of zones and derives decentralized rebalancing strategies for each zone by applying a graph neural network (GNN) based multi-agent reinforcement learning (MARL). The proposed algorithm first represents the overall state of the fab into a directed graph and uses the graph representation to construct embedding values for each zone. The node embedding values are then used to determine the rebalancing action from each zone in a decentralized manner but to induce cooperation among zones. Simulation studies have shown that the proposed algorithm is effective in increasing various system-level key performance metrics compared to other heuristic and learning-based rebalancing strategies.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Issue Date
2019-12-12
Language
English
Citation

58th IEEE Conference on Decision and Control, CDC 2019, pp.132 - 138

DOI
10.1109/CDC40024.2019.9030245
URI
http://hdl.handle.net/10203/280198
Appears in Collection
IE-Conference Papers(학술회의논문)
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