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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.18054 |
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| _version_ | 1866909963922702336 |
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| author | S, Nikhil N Muhammed, Bilal Beemaraj, Soban Babu Joshi, Amol Dilip |
| author_facet | S, Nikhil N Muhammed, Bilal Beemaraj, Soban Babu Joshi, Amol Dilip |
| contents | Selecting a solution algorithm for the Facility Layout Problem (FLP), an NP-hard optimization problem with multiobjective trade-off, is a complex task that requires deep expert knowledge. The performance of a given algorithm depends on the specific characteristics of the problem, such as the number of facilities, objectives, and constraints. This creates a need for a data-driven recommendation method to guide algorithm selection in automated design systems. This paper introduces a new recommendation method to make this expertise accessible, based on a Knowledge Graph-Based Retrieval-Augmented Generation (KG-RAG) framework. In this framework, a domain-specific knowledge graph (KG) is constructed from the literature. The method then employs a multifaceted retrieval mechanism to gather relevant evidence from this KG using three distinct approaches: precise graph-based search, flexible vector-based search, and cluster-based high-level search. The retrieved evidence is utilized by a Large Language Model (LLM) to generate algorithm recommendations based on data-driven reasoning. This KG-RAG framework is tested on a use case consisting of six problems comprising of complex multi-objective and multi-constraint FLP case. The results are compared with the Gemini 1.5 Flash chatbot. The results show that KG-RAG achieves an average reasoning score of 4.7 out of 5 compared to 3.3 for the baseline chatbot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18054 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Knowledge Graph-based Retrieval-Augmented Generation Framework for Algorithm Selection in the Facility Layout Problem S, Nikhil N Muhammed, Bilal Beemaraj, Soban Babu Joshi, Amol Dilip Information Retrieval Artificial Intelligence Machine Learning Selecting a solution algorithm for the Facility Layout Problem (FLP), an NP-hard optimization problem with multiobjective trade-off, is a complex task that requires deep expert knowledge. The performance of a given algorithm depends on the specific characteristics of the problem, such as the number of facilities, objectives, and constraints. This creates a need for a data-driven recommendation method to guide algorithm selection in automated design systems. This paper introduces a new recommendation method to make this expertise accessible, based on a Knowledge Graph-Based Retrieval-Augmented Generation (KG-RAG) framework. In this framework, a domain-specific knowledge graph (KG) is constructed from the literature. The method then employs a multifaceted retrieval mechanism to gather relevant evidence from this KG using three distinct approaches: precise graph-based search, flexible vector-based search, and cluster-based high-level search. The retrieved evidence is utilized by a Large Language Model (LLM) to generate algorithm recommendations based on data-driven reasoning. This KG-RAG framework is tested on a use case consisting of six problems comprising of complex multi-objective and multi-constraint FLP case. The results are compared with the Gemini 1.5 Flash chatbot. The results show that KG-RAG achieves an average reasoning score of 4.7 out of 5 compared to 3.3 for the baseline chatbot. |
| title | A Knowledge Graph-based Retrieval-Augmented Generation Framework for Algorithm Selection in the Facility Layout Problem |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.18054 |