Constraint‑Guided Discrete Diffusion for Conditional Hierarchical Industrial Graph Generation

Industrial production systems must continuously adapt to changes in product mix, machine states, and workforce availability. The ability to swiftly generate new production system layouts and process plans to respond to disruptions is essential. This work introduces a breakthrough two-stage constraint-guided diffusion model that realizes fully automatic, hierarchical industrial layout generation with strict feasibility guarantees. An automatic synthesis is developed by combining a plant-level flow graph with processing stations, buffer stations, assembly and disassembly stations, together with a station-level graph capturing the detailed behavior for each station. The framework trains two discrete diffusion models: one learns the global topology, and the other, conditioned on station type, learns internal Petri net representations for individual stations. A projector is defined to enforce a set of structural and dynamic constraints at every denoising step. The method delivers 100% validity under three progressively constrained inventory scenarios and preserves 99% uniqueness according to the Weisfeiler–Lehman hash. A complete hierarchical layout can be generated in approximately 2 seconds, and simulation-based evaluations confirm the operational competitiveness of the generated designs.