Frequent order personalisation and disruptions in manufacturing are increasing intra-logistics complexity, posing greater challenges for intra-logistics Vehicle Routing Problem (VRP) resolution. VRP models are prone to structural changes, such as objective modification or constraint addition/removal, whilst knowledge reusability for model updates remains limited. Although emerging large language model (LLM) technology offers new potential for addressing this challenge, current limitations include unclear internal self-regulation mechanisms, difficulties in integrating and reusing modelling knowledge, and poor adaptability of outputs to real-world tasks. To address these issues, this study proposes a knowledge-augmented LLM framework for intra-logistics VRP optimisation modelling in digital manufacturing. It designs a self-regulating assisted modelling operational mode and a multi-step modelling knowledge augmentation method, leveraging Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) technologies to provide interactive intelligent assistance for intra-logistics VRP model reconstruction. Case experiment results demonstrate that the framework can efficiently reuse modelling knowledge and offer effective assistance to practitioners.
Chen et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: