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

2025-12-15

AI Summary

Coupling assembly sequence planning (ASP) and production line layout planning (PLP) remains challenging due to combinatorial explosion, insufficient integration of geometric constraints (e.g., kinematics, collision, tolerances, joint compatibility), and lack of unified modeling. Method: This paper proposes PyCAALP—a graph-based framework that jointly models ASP and PLP; incorporates multi-source geometric constraints into a unified representation; and introduces a geometry-driven heuristic pruning strategy alongside modular mixed-integer programming (MIP) formulation to balance feasibility guarantees and computational efficiency. The framework supports customizable engineering constraints and explicit ASP–PLP trade-off optimization. Contribution/Results: PyCAALP is open-source and industrially scalable. Experiments on complex assemblies demonstrate significant reduction in MIP solving time while ensuring constraint completeness and practical planning validity.

Technology Category

• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Constraint Satisfaction and Optimization: Constraint Programming
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Automates assembly sequence and production line planning using graph-based optimization
• Integrates kinematic constraints and geometric feasibility for automated assembly
• Formulates production line planning as a Mixed-Integer Program to balance station times

Innovation

• Graph-based optimization for automated assembly planning
• Mixed-Integer Program balancing manufacturing station times
• Heuristics and constraints to reduce combinatorial complexity