Trajectory-Initialized Neural Double Q-Routing for Large-Scale Overhead Hoist Transport Systems
Large-scale industrial robot fleets share constrained physical infrastructure, making vehicle travel times dependent on safety separation, intersection access, downstream blocking, and station contention. We study this problem in overhead hoist transport (OHT) systems, a representative ceiling-mounted material-handling system used in semiconductor fabs. Static shortest-path routing cannot account for these time-varying traffic costs, whereas tabular Q-routing adapts online but learns each destination--node--action value independently, limiting information sharing across sparsely visited routin
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- PossiblePossibly related (embedding) · 52%Adaptive primal–dual Q-learning for electric vehicle route optimization on real-world charging networks - Nature →
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
- FuzzyOverlapping authors or contributors · 62%hiyouga/LlamaFactory →
“Shared author/contributor keys: lin”
- LinkedLinked via arxiv author · 85%Hongcheng Guo →
“Trajectory-Initialized Neural Double Q-Routing for Large-Scale Overhead Hoist Transport Systems”
- LinkedLinked via arxiv author · 85%Qiusheng Zhao →
“Trajectory-Initialized Neural Double Q-Routing for Large-Scale Overhead Hoist Transport Systems”
- LinkedLinked via arxiv author · 85%Anbang Liu →
“Trajectory-Initialized Neural Double Q-Routing for Large-Scale Overhead Hoist Transport Systems”
- LinkedLinked via arxiv author · 85%Shaochong Lin →
“Trajectory-Initialized Neural Double Q-Routing for Large-Scale Overhead Hoist Transport Systems”