A Self-Evolving Framework for Automated Constraint Programming Model Construction in Multi-Robot Task Allocation and Scheduling

Abstract

Multi-robot task allocation and scheduling (MRTAS) in aerospace manufacturing is highly challenging due to the coupled physical, logical, and resource constraints inherent in complex sequential operations. Traditional exact algorithms face scalability limitations, whereas manual constraint programming (CP) modeling requires extensive domain expertise and lengthy development cycles, hindering their adaptation to dynamic production requirements. To address these challenges, a retrieval-augmented, self-evolving framework based on in-context learning and streaming large language models (LLMs), referred to as MRTAS-ICL-StreamLLM, is developed to autonomously generate high-quality CP models for MRTAS problems. The proposed framework comprises three synergistic components: a domain-adaptive retrieval mechanism that dynamically selects relevant demonstration examples via hybrid similarity measures combining lexical and structural features; an iterative constraint refinement module that employs temperature annealing to progressively generate domainspecific optimization rules while balancing pattern exploitation and novel exploration; and a code execution feedback loop that validates the generated models against actual solver performance. This closed-loop architecture transforms LLMs into robust modeling assistants capable of continuous learning from execution outcomes. Experimental validation in aircraft skin manufacturing scenarios demonstrates that MRTAS-ICL-StreamLLM generates accurate and executable CP models while adaptively improving solution quality through iterative refinement, thereby offering an effective approach to automated and adaptive CP modeling for complex industrial scheduling environments.