Author

Zuming Liu

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Preprint Aug 2026

PLAN: Parallel Liquid-Inspired Approximation Network for Efficient Representation Learning in Flexible Job Shop Scheduling

This work proposes PLAN (Parallel Liquid-inspired Approximation Network), a lightweight representation learning framework that reformulates continuous liquid-state dynamics into a discretized and parallelizable formulation and acts as a versatile, plug-and-play backbone that generalizes to complex FJSP variants.

D. D. Kannan, Wei Zhang, Jieyi Bi et al. · 0 citations