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

TL;DR

This paper introduces PyCAALP, an open-source, graph-based framework for automated assembly sequence and production line planning that integrates geometric constraints, heuristics, and optimization techniques to handle complex manufacturing scenarios.

Contribution

It presents a novel unified framework combining assembly sequence planning and production line optimization using graph models and mixed-integer programming.

Findings

Framework computes all feasible production sequences.

Incorporates geometric and kinematic constraints effectively.

Reduces computational time with heuristic techniques.

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. While some complexity reduction techniques may sacrifice optimality, they significantly reduce the MIPs computational time. 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.