Learning-Assisted Congestion-Aware Route Scheduling for Semiconductor Fab Material Control Systems
Automated material handling systems in semiconductor fabs are operated by a material control system (MCS) that must schedule a relay route for every transport command online, before execution. This is a data-driven scheduling problem in which route cost is dominated in the upper tail by queueing at heterogeneous, partially observable relay equipment, so route selection requires estimating both delivery time and congestion risk at the decision moment. This paper proposes a transport-network-aware dynamic congestion representation (TN-DCR). Built on a static directed transport graph induced by h
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- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet-v1-1 →
“Fuzzy title match (0.94): “Learning-Assisted Congestion-Aware Route Scheduling for Semi” ≈ “lllyasviel/ControlNet-v1-1””
- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet →
“Fuzzy title match (0.94): “Learning-Assisted Congestion-Aware Route Scheduling for Semi” ≈ “lllyasviel/ControlNet””
- PossiblePossibly related (embedding) · 47%Machine and Deep Learning Advance Traffic Congestion Forecasting in Intelligent Transportation Systems - Bioengineer.org →
- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
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“Shared author/contributor keys: yin”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%hiyouga/LlamaFactory →
“Shared author/contributor keys: lin”
- LinkedLinked via arxiv author · 85%Hao Yin →
“Learning-Assisted Congestion-Aware Route Scheduling for Semiconductor Fab Material Control Systems”