*Result*: Facility layout optimisation of an intelligent manufacturing unit based on queueing network and deep reinforcement learning.

*Facility layout critically affects manufacturing efficiency and cost. Fixed layouts struggle to adapt to uncertain demand, constraining system performance. In facility layout problems (FLPs) under demand uncertainty, traditional metaheuristics suffer from limited responsiveness to production changes. Therefore, we propose a deep reinforcement learning (DRL)-based method capable of handling high-dimensional inputs and exhibiting cross-scenario generalisation. Specifically, we model the FLP as a Markov Decision Process (MDP) and leverage Transformer-based self-attention mechanism combined with Pointer Network to enable adaptive decision-making. Considering inherent stochasticity in manufacturing systems, the system is modelled as an open queueing network with finite buffers using the Generalised Expansion Method (GEM). Compared with analytical or simulation models, the queueing network model enables rapid estimation of real-world production performance, providing efficient reward feedback for DRL training. Numerical experiments validate that the proposed method achieves the best performance among baselines, with an average improvement of 6.89% in large-scale instance. Generalisation tests show that integration with the 2-opt heuristic enables swift generation of high-quality layouts in unseen scenarios. A real-world case study further validates the framework's applicability, offering a practical solution for dynamic layout reconfiguration in intelligent manufacturing, and significantly enhancing production flexibility and resource utilisation under demand uncertainty. [ABSTRACT FROM AUTHOR]

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