Academic Journal
Using large language models (LLMs) to support simulation-based optimization in supply chain management.
| Τίτλος: | Using large language models (LLMs) to support simulation-based optimization in supply chain management. |
|---|---|
| Συγγραφείς: | Wiśniewski, T. |
| Πηγή: | Advances in Production Engineering & Management; Dec2025, Vol. 20 Issue 4, p491-506, 16p |
| Θεματικοί όροι: | Supply chain management, Artificial intelligence, ChatGPT, Language models, Data analysis, Natural language processing, Mathematical optimization |
| Περίληψη: | The emergence of Artificial Intelligence (AI) in Supply Chain Management (SCM) heralds a transformative shift, breaking traditional barriers and unlock- ing new opportunities for optimization and efficiency. This study explores the impact of artificial intelligence, particularly large language models (LLMs), on simulation-based optimization applications in supply chain management. The novelty of LLMs lies in their ability to enhance both the technical and practical aspects of simulation-based optimization. On the technical side, LLMs can assist in constructing and fine-tuning optimization models by analyzing historical data, identifying patterns, and generating recommendations for optimal strategies. On the practical side, these models have the potential to simplify complex methodologies, making them more comprehensible and actionable for practitioners without extensive expertise in AI or advanced analytics. The article presents practical implications of LLMs in the form of a ChatGPT-based application, in which users express their supply chain challenges in natural language, and the model responds with tailored optimization strategies or simulation scenarios. The presented examples demonstrate how LLMs can automatically generate simulation models and support optimization processes in typical supply chain management scenarios. These results are preliminary and highlight both the potential of this approach and its current limitations, including occasional inaccuracies in the generated code. [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Production Engineering & Management is the property of Production Engineering Institute (PEI), University of Maribor and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Using large language models (LLMs) to support simulation-based optimization in supply chain management. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wiśniewski%2C+T%2E%22">Wiśniewski, T.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Advances in Production Engineering & Management; Dec2025, Vol. 20 Issue 4, p491-506, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Supply+chain+management%22">Supply chain management</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22ChatGPT%22">ChatGPT</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The emergence of Artificial Intelligence (AI) in Supply Chain Management (SCM) heralds a transformative shift, breaking traditional barriers and unlock- ing new opportunities for optimization and efficiency. This study explores the impact of artificial intelligence, particularly large language models (LLMs), on simulation-based optimization applications in supply chain management. The novelty of LLMs lies in their ability to enhance both the technical and practical aspects of simulation-based optimization. On the technical side, LLMs can assist in constructing and fine-tuning optimization models by analyzing historical data, identifying patterns, and generating recommendations for optimal strategies. On the practical side, these models have the potential to simplify complex methodologies, making them more comprehensible and actionable for practitioners without extensive expertise in AI or advanced analytics. The article presents practical implications of LLMs in the form of a ChatGPT-based application, in which users express their supply chain challenges in natural language, and the model responds with tailored optimization strategies or simulation scenarios. The presented examples demonstrate how LLMs can automatically generate simulation models and support optimization processes in typical supply chain management scenarios. These results are preliminary and highlight both the potential of this approach and its current limitations, including occasional inaccuracies in the generated code. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Advances in Production Engineering & Management is the property of Production Engineering Institute (PEI), University of Maribor and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.14743/apem2025.4.554 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 491 Subjects: – SubjectFull: Supply chain management Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: ChatGPT Type: general – SubjectFull: Language models Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Mathematical optimization Type: general Titles: – TitleFull: Using large language models (LLMs) to support simulation-based optimization in supply chain management. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wiśniewski, T. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 18546250 Numbering: – Type: volume Value: 20 – Type: issue Value: 4 Titles: – TitleFull: Advances in Production Engineering & Management Type: main |
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