用图优化自动规划装配顺序与产线，兼顾可行性与效率。
A Unified Framework for Automated Assembly Sequence and Production Line Planning using Graph-based Optimization
- 基于图模型建模组件和连接关系，整合运动学约束确保可装配性。
- 通过子图缩减将复杂产线规划速度提升至原方案的千倍以内。
- 支持自定义工程约束，适合复杂工业装配系统设计与优化。
本文提出PyCAALP（基于Python的计算机辅助装配线规划），一种用于自动化装配序列规划（ASP）与产线规划（PLP）的统一框架，采用图结构建模生产模块中的组件与连接关系。该框架集成运动学边界条件（如部件间碰撞可能），保障装配规划的可行性。算法计算所有可行生产序列，整合模块以检测空间关系并建立几何约束。同时引入抓取可行性、公差匹配与接头兼容性等属性，应对装配序列生成中的高组合复杂度。通过单件流装配与几何约束强化等启发式方法进一步缩小解空间，提升复杂装配的规划效率。产线规划阶段被建模为混合整数规划（MIP），在固定数量制造工位下平衡总工时。为保持复杂装配下的可求解性，采用确定性路径引导的缩减策略，在由完整高质量装配路径构建的子图上求解MIP，该方法保证可行性并几乎精确复现全图最优解，同时将计算量降至原图的极小部分，实现高达三个数量级的速度提升。框架支持工程约束自定义，可在ASP与PLP间灵活权衡。开源项目已发布于https://github.com/TUM-utg/PyCAALP，促进工业与制造研究领域的协作与应用。
原文摘要 · Abstract (English)
This paper presents PyCAALP (Python-based Computer-Aided Assembly Line Planning), a framework for automated Assembly Sequence Planning (ASP) and Production Line Planning (PLP), employing a graph-based approach to model components and joints within production modules. The framework integrates kinematic boundary conditions, such as potential part collisions, to guarantee the feasibility of automated assembly planning. The developed algorithm computes all feasible production sequences, integrating modules for detecting spatial relationships and formulating geometric constraints. The algorithm incorporates additional attributes, including handling feasibility, tolerance matching, and joint compatibility, to manage the high combinatorial complexity inherent in assembly sequence generation. Heuristics, such as Single-Piece Flow assembly and geometrical constraint enforcement, are utilized to further refine the solution space, facilitating more efficient planning for complex assemblies. The PLP stage is formulated as a Mixed-Integer Program (MIP), balancing the total times of a fixed number of manufacturing stations. To keep the MIP tractable for complex assemblies, a deterministic path-guided reduction solves it on a subgraph assembled from complete, high-quality assembly paths, which preserves feasibility by construction and reproduces the full-graph optimum, exactly or near-exactly, at a small fraction of the directed graph and with speedups of up to three orders of magnitude. Furthermore, the framework enables customization of engineering constraints and supports a flexible trade-off between ASP and PLP. The open-source nature of the framework, available at https://github.com/TUM-utg/PyCAALP, promotes further collaboration and adoption in both industrial and production research applications.
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