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

2025-10-02

AI Summary

To address the manual, error-prone, and inefficient modeling of operational scheduling constraints in manufacturing, this paper proposes an automated constraint generation method integrating generative models with domain knowledge. The core innovation lies in a three-tier hierarchical structural space, coupled with a domain-specific language (DSL) and layered semantic mapping, to structurally guide large language models (LLMs). An adaptive scenario-aware algorithm is further designed to enhance contextual alignment. This approach significantly improves the accuracy, robustness, and interpretability of generated constraints, effectively mitigating inherent LLM limitations—namely, natural language ambiguity and output non-determinism. Experimental results demonstrate substantial gains over pure-LLM baselines: constraint generation accuracy increases by up to 32.7%, and cross-scenario generalization capability improves markedly. The method thus enables reliable deployment of Advanced Planning and Scheduling (APS) systems in complex, dynamic manufacturing environments.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Constraint Satisfaction and Optimization: Constraint Programming
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Automating constraint specification from manufacturing data
• Regulating generative models for reliable scheduling constraints
• Adapting constraint architecture to specific production scenarios

Innovation

• Regulates LLMs with domain-specific hierarchical representation
• Automates constraint specification via structured manufacturing data
• Customizes architecture with production scenario adaptation algorithm