Summary of Automated Conversion Of Static to Dynamic Scheduler Via Natural Language, by Paul Mingzheng Tang et al.
Automated Conversion of Static to Dynamic Scheduler via Natural Language
by Paul Mingzheng Tang, Kenji Kah Hoe Leong, Nowshad Shaik, Hoong Chuin Lau
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to dynamic scheduling problems using Large Language Models (LLMs). The authors aim to automate the process of implementing constraints for Dynamic Scheduling (RAGDyS) without requiring optimization modeling expertise. To achieve this, they develop a Retrieval-Augmented Generation (RAG) based LLM model that can learn from existing static models and generate code for dynamic scheduling problems given natural language constraint descriptions. The proposed framework aims to simplify the process of creating new schedules by minimizing technical complexities and computational workload for end-users. The authors leverage LLMs to minimize the need for optimization modeling expertise, allowing users to quickly obtain a new schedule close to the original with changes reflected in the constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create new schedules automatically without needing to be an expert in math or coding. This paper explores how Large Language Models (LLMs) can help make this possible. By using these models, we can teach a computer to understand and generate code for scheduling problems given simple language descriptions. This means that anyone can easily get a new schedule close to the original one, with changes reflected in the constraints. The goal is to make it easier for people without extensive knowledge of math or programming to create new schedules quickly and efficiently. |
Keywords
» Artificial intelligence » Optimization » Rag » Retrieval augmented generation
