Engineering drawings are fundamental to industries such as oil and gas, construction, and manufacturing. However, current practices relying on manual design or rigid parametric templates often suffer from inefficiency and layout inconsistencies. To address these issues, the layout task is formulated as the Orthogonal Rectangle Packing Problem with Multiple Configurations and Complex Constraints (ORPPMC). The Deep Reinforcement Learning for Multi-Configuration Drawing Layout (DRL-MCDL) framework is proposed, which integrates the Pointer Network for Drawing Element Sequencing (PN-DES) with the Target-Type-Matching-based Multi-Pattern Positioning Strategy (TTM-MPPS). Within this framework, PN-DES employs deep reinforcement learning and feature fusion to combine element attributes with layout configurations for optimal sequence inference, while TTM-MPPS performs precise positioning in accordance with industrial rules to ensure strict adherence to aesthetic requirements. Ablation experiments validate the contribution of each module. Experimental results on real-world engineering drawings demonstrate that DRL-MCDL achieves a Feasibility Rate (FR) exceeding 98.5% on standard instances (12–40 elements), significantly outperforming traditional methods. Furthermore, it maintains a high inference efficiency with an Average Time (AT) of less than 0.3 s, striking an optimal balance between layout quality and computational speed.
Sun et al. (Tue,) studied this question.