A Unified Framework for Automated Assembly Sequence and Production Line Planning using Graph-based Optimization

Abstract: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 this https URL, promotes further collaboration and adoption in both industrial and production research applications.

Robotics (cs.RO); Mathematical Software (cs.MS)

F.2.2; I.2.8; G.2.2

arXiv:2512.13219 [cs.RO]

(or arXiv:2512.13219v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2512.13219 Focus to learn more arXiv-issued DOI via DataCite

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