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NSGA-II algorithm for assembly plans optimization using the model-based systems engineering
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AI
DOI：10.1007/s12008-026-02586-6.png)
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Abstract
En 中文
Assembly Sequence Planning (ASP) is a critical stage in product development that strongly influences manufacturing performance. However, communication gaps between product designers and system engineers often lead to suboptimal assembly plans, as Computer-Aided Design (CAD)-based proposals may not fully align with Model-Based Systems Engineering (MBSE) requirements. To address this challenge, this study proposes a collaborative CAD-MBSE assembly planning optimization methodology that integrates both domains within a unified decision-making framework. This approach enables bidirectional information exchange between designers and system engineers, allowing geometric feasibility and system-level constraints to be jointly considered during assembly sequence generation. Furthermore, a multi-objective optimization process based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to balance multiple assemblability indicators, enabling the identification of requirement-compliant assembly sequences. The effectiveness of the methodology is demonstrated through a vibration generator case study, which shows improved coordination between design and system models, enhanced assembly quality, and a reduction in the number of candidate sequences while maintaining compliance with design requirements. These results highlight the value of multi-criteria evaluation and collaborative decision-making in achieving efficient and consistent assembly planning outcomes.
Keywords:
MBSE requirements
CAD model
Assembly sequence planning
Assemblability indicators
Optimization
NSGA-II algorithm
Journal
I
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2.5
Papers:
168
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0
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