GenOR-Twin: A Semantic Middleware for Integrating Operational Discourse with Mathematical Optimization

2026-09-11

AI Summary

本文提出GenOR-Twin框架，利用大语言模型作为语义翻译器将非结构化的操作日志转化为数学优化问题，并通过动态约束注入机制实时调整优化问题，以适应实际操作环境中的不确定性。

Abstract

We introduce GenOR-Twin, a neuro-symbolic framework that bridges the translation gap between unstructured operational logs and rigorous mathematical optimization. Our architecture uniquely positions Large Language Models as semantic translators rather than direct solvers, ensuring that the system retains the feasibility guarantees of exact combinatorial methods. { \color{red}We design a dynamic constraint injection mechanism (the runtime translation of qualitative disruption events into formal mathematical constraints) that allows the system to structurally modify the optimization problem's feasibility region in real-time based on qualitative human inputs. The resulting bidirectional coupling---where operational observations update the virtual model state and optimized decisions are reflected back into the Knowledge Graph---satisfies the synchronization requirement of a proper Digital Twin. The framework features an adaptive decision policy} that automatically selects between low-complexity schedule repair and full re-optimization by analyzing the available system slack. Finally, we demonstrate the generalization of this approach across six distinct optimization domains, {\color{red}turning static models into resilient systems that adapt to the operational uncertainty and variability of real-world environments.}

Problem

• Semantic Middleware
• Operational Logs
• Mathematical Optimization
• Constraint Injection
• Digital Twin

Innovation

• dynamic constraint injection
• neuro-symbolic framework
• adaptive decision policy
• real-time adjustment
• resilient systems