An Enhanced RAG–LLM Framework for Decision Support in Sustainable Industrial Engineering and Management

Abstract

Retrieval-augmented generation (RAG) with large language models (LLMs) provides a new technical pathway for integrating industrial-domain knowledge with the reasoning capabilities of LLMs and has substantial application potential in sustainable industrial decision support. However, existing RAG-based decision-support methods focus mainly on enhancing semantic relevance and remain inadequate in industrial knowledge governance, the participation of retrieved evidence throughout the decision process, and the validation of decision results and feedback before implementation, thereby limiting the accuracy, traceability, and reliability of decision outcomes. To address these problems, this study proposes a RAG–LLM framework for sustainable industrial decision support. The framework generates governed knowledge units with explicit evidence boundaries from multi-source heterogeneous industrial data and combines hybrid retrieval, evidence packaging, and validation and feedback mechanisms to enable reliable industrial-knowledge retrieval and controllable decision generation. Simulated experiments were conducted in sustainable industrial engineering and management decision scenarios covering four representative tasks: equipment-fault diagnosis, quality-anomaly analysis, energy optimization, and wastewater-compliance management. The results show that the proposed method effectively improves evidence-retrieval performance and content coverage. With the hybrid retrieval strategy, mean Recall@6 increased from 0.7917 for BM25 to 1.0000, mean nDCG@6 increased from 0.8398 to 1.0000, and mean GVR@6 decreased from 0.1667 to 0. The enhanced RAG–LLM maintained high gold-standard source coverage across different LLMs, with mean evidence source-ID coverage values of 1.0000, 0.9917, and 0.9750 for the ChatGPT 5.5, Gemini 3.5 Flash, and DeepSeek V4 Flash combinations, respectively. After feedback regulation before decision implementation was introduced, the mean evidence source-ID coverage for representative task responses increased from 0.4167 to 1.0000. The mechanism that binds source-labeled evidence throughout the decision process further improved the verifiability of generated results and the traceability of decision evidence, indicating that the enhanced RAG–LLM framework can provide reliable decision support for sustainable industrial engineering and management. Future research will further explore real-time multimodal data integration, privacy-preserving deployment, and long-term validation in real industrial environments.