# Automated Constraint Specification for Job Scheduling by Regulating Generative Model with Domain-Specific Representation

arXiv: https://arxiv.org/abs/2510.02679 · HTML: https://mlsanity.com/paper/2510.02679/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 76 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [An Empirical Study of the Non-determinism of ChatGPT in Code Generation](https://mlsanity.com/paper/2308.02828/) (2023)
- [NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions](https://mlsanity.com/paper/2303.08233/) (2023)
- [ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling](https://mlsanity.com/paper/2405.17743/) (2024)
- [OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models](https://mlsanity.com/paper/2402.10172/) (2024)
- [LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch](https://mlsanity.com/paper/2410.13213/) (2024)
- [Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions](https://mlsanity.com/paper/2209.15565/) (2022)
- [Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models](https://mlsanity.com/paper/2311.08921/) (2023)
- [Decision Information Meets Large Language Models: The Future of Explainable Operations Research](https://mlsanity.com/paper/2502.09994/) (2025)
- [AutoDSL: Automated domain-specific language design for structural representation of procedures with constraints](https://mlsanity.com/paper/2406.12324/) (2024)
- [Hierarchically Encapsulated Representation for Protocol Design in Self-Driving Labs](https://mlsanity.com/paper/2504.03810/) (2025)
- [Expert-level protocol translation for self-driving labs](https://mlsanity.com/paper/2411.00444/) (2024)
- [On the Complexity of Bayesian Generalization](https://mlsanity.com/paper/2211.11033/) (2022)
- [Abstract Hardware Grounding towards the Automated Design of Automation Systems](https://mlsanity.com/paper/2410.05663/) (2024)
- [Constraint representation towards precise data-driven storytelling](https://mlsanity.com/paper/2410.07535/) (2024)
- [Targeted control of fast prototyping through domain-specific interface](https://mlsanity.com/paper/2506.11070/) (2025)

## Built on by (most cited first, 1 of 1)

- [BioPIE: A Biomedical Protocol Information Extraction Dataset for Experiment Understanding](https://mlsanity.com/paper/2601.04524/) (2026)

## Abstract (arXiv)

Advanced Planning and Scheduling (APS) systems have become indispensable for modern manufacturing operations, enabling optimized resource allocation and production efficiency in increasingly complex and dynamic environments. While algorithms for solving abstracted scheduling problems have been extensively investigated, the critical prerequisite of specifying manufacturing requirements into formal constraints remains manual and labor-intensive. Although recent advances of generative models, particularly Large Language Models (LLMs), show promise in automating constraint specification from heterogeneous raw manufacturing data, their direct application faces challenges due to natural language ambiguity, non-deterministic outputs, and limited domain-specific knowledge. This paper presents a constraint-centric architecture that regulates LLMs to perform reliable automated constraint specification for production scheduling. The architecture defines a hierarchical structural space organized across three levels, implemented through domain-specific representation to ensure precision and reliability while maintaining flexibility. Furthermore, an automated production scenario adaptation algorithm is designed and deployed to efficiently customize the architecture for specific manufacturing configurations. Experimental results demonstrate that the proposed approach successfully balances the generative capabilities of LLMs with the reliability requirements of manufacturing systems, significantly outperforming pure LLM-based approaches in constraint specification tasks.