Design Structure Matrix Modularization with Large Language Models
Design Structure Matrix (DSM) modularization, the task of partitioning system elements into cohesive modules, is a fundamental combinatorial challenge in engineering design. Traditional methods treat modularization as a pure graph optimization, without access to the engineering context embedded in the system. Building on prior work on LLM-based combinatorial optimization for DSM sequencing, this paper extends the method to modularization across five cases and three backbone LLMs. Our method achieves near-reference quality within 30 iterations without requiring specialized optimization code. Counterintuitively, domain knowledge, beneficial in sequencing, consistently impairs performance on...
| Authors | Shuo Jiang, Jianxi Luo |
|---|---|
| Primary category | cs.CE |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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