Integrating Large Language Models and Optimization in Semi- Structured Decision Making: Methodology and a Case Study
Semi-structured decisions, which fall between highly structured and unstructured decision types, rely on human intuition and experience for the final choice, while using data and analytical models to generate tentative solutions. These processes are traditionally iterative and time-consuming, requir...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/12/582 |
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| author | Gianpaolo Ghiani Gianluca Solazzo Gianluca Elia |
| author_facet | Gianpaolo Ghiani Gianluca Solazzo Gianluca Elia |
| author_sort | Gianpaolo Ghiani |
| collection | DOAJ |
| description | Semi-structured decisions, which fall between highly structured and unstructured decision types, rely on human intuition and experience for the final choice, while using data and analytical models to generate tentative solutions. These processes are traditionally iterative and time-consuming, requiring cycles of data gathering, analysis, and option evaluation. In this study, we propose a novel framework that integrates Large Language Models (LLMs) with optimization techniques to streamline such decision-making processes. In our approach, LLMs leverage their capabilities in data interpretation, common-sense reasoning, and mathematical modeling to assist decision makers by reducing cognitive load. They achieve this by automating aspects of information processing and option evaluation, while preserving human oversight as a crucial component of the final decision-making process. Another significant strength of our framework lies in its potential to drive the evolution of a new generation of decision support systems (DSSs). Unlike traditional systems that rely on rigid and inflexible interfaces, our approach enables users to express their preferences in a more natural, intuitive, and adaptable manner, substantially enhancing both usability and accessibility. A case study on last-mile delivery system design in a smart city demonstrates the practical application of this framework. The results suggest that our approach has the potential to simplify the decision-making process and improve efficiency by reducing cognitive load, enhancing user experience, and facilitating more intuitive interactions. |
| format | Article |
| id | doaj-art-fa59fa49ab8147f0a216ce9898d05b76 |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-fa59fa49ab8147f0a216ce9898d05b762025-08-20T02:53:27ZengMDPI AGAlgorithms1999-48932024-12-01171258210.3390/a17120582Integrating Large Language Models and Optimization in Semi- Structured Decision Making: Methodology and a Case StudyGianpaolo Ghiani0Gianluca Solazzo1Gianluca Elia2Dipartimento di Ingegneria dell’Innovazione, Università del Salento, Via per Monteroni, 73100 Lecce, ItalyDipartimento di Ingegneria dell’Innovazione, Università del Salento, Via per Monteroni, 73100 Lecce, ItalyDipartimento di Ingegneria dell’Innovazione, Università del Salento, Via per Monteroni, 73100 Lecce, ItalySemi-structured decisions, which fall between highly structured and unstructured decision types, rely on human intuition and experience for the final choice, while using data and analytical models to generate tentative solutions. These processes are traditionally iterative and time-consuming, requiring cycles of data gathering, analysis, and option evaluation. In this study, we propose a novel framework that integrates Large Language Models (LLMs) with optimization techniques to streamline such decision-making processes. In our approach, LLMs leverage their capabilities in data interpretation, common-sense reasoning, and mathematical modeling to assist decision makers by reducing cognitive load. They achieve this by automating aspects of information processing and option evaluation, while preserving human oversight as a crucial component of the final decision-making process. Another significant strength of our framework lies in its potential to drive the evolution of a new generation of decision support systems (DSSs). Unlike traditional systems that rely on rigid and inflexible interfaces, our approach enables users to express their preferences in a more natural, intuitive, and adaptable manner, substantially enhancing both usability and accessibility. A case study on last-mile delivery system design in a smart city demonstrates the practical application of this framework. The results suggest that our approach has the potential to simplify the decision-making process and improve efficiency by reducing cognitive load, enhancing user experience, and facilitating more intuitive interactions.https://www.mdpi.com/1999-4893/17/12/582semi-structured decisionshuman-in-the-loopknowledge discoverylarge language modelslast-mile logistics |
| spellingShingle | Gianpaolo Ghiani Gianluca Solazzo Gianluca Elia Integrating Large Language Models and Optimization in Semi- Structured Decision Making: Methodology and a Case Study Algorithms semi-structured decisions human-in-the-loop knowledge discovery large language models last-mile logistics |
| title | Integrating Large Language Models and Optimization in Semi- Structured Decision Making: Methodology and a Case Study |
| title_full | Integrating Large Language Models and Optimization in Semi- Structured Decision Making: Methodology and a Case Study |
| title_fullStr | Integrating Large Language Models and Optimization in Semi- Structured Decision Making: Methodology and a Case Study |
| title_full_unstemmed | Integrating Large Language Models and Optimization in Semi- Structured Decision Making: Methodology and a Case Study |
| title_short | Integrating Large Language Models and Optimization in Semi- Structured Decision Making: Methodology and a Case Study |
| title_sort | integrating large language models and optimization in semi structured decision making methodology and a case study |
| topic | semi-structured decisions human-in-the-loop knowledge discovery large language models last-mile logistics |
| url | https://www.mdpi.com/1999-4893/17/12/582 |
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