PLAN: A Parallel Liquid-Inspired Network for Efficient FJSP Representation Learning

A new framework called PLAN (Parallel Liquid-inspired Approximation Network) has been developed by researchers to improve efficiency in deep reinforcement learning (DRL) for flexible job shop scheduling (FJSP). Conventional DRL methods depend on attention-focused architectures that, despite their impressive performance, face issues with high parameter counts and significant inference delays as problem sizes increase. Liquid neural networks (LNNs) provide a more parameter-efficient solution through adaptive state evolution, but their sequential nature hampers computational efficiency. PLAN addresses this by transforming continuous liquid-state dynamics into a format that allows for parallel processing. It separates state evolution from context aggregation, using liquid-inspired updates for the evolving state and a lightweight module for global context. This structure minimizes the computational burden while ensuring effective representation learning. The paper detailing PLAN can be found on arXiv with the identifier 2608.03041, highlighting its potential to tackle scalability issues in FJSP, a vital area in manufacturing and operations research, ultimately improving the feasibility of DRL-based scheduling in practical scenarios.