用大模型提升运筹学决策解释力，让优化结果更透明可信。
Decision Information Meets Large Language Models: The Future of Explainable Operations Research
- 提出决策信息框架，通过敏感性分析量化约束变化影响
- 结合二部图与大模型，实现可操作的解释生成
- 建立首个工业级评测基准，推动运筹学透明化
运筹学在众多行业决策中至关重要。尽管近年来通过整合大语言模型（LLMs）显著提升了自动化与效率，但现有方法仍难以生成有意义的解释，影响了应用中的透明度与可信度。为解决此问题，本文提出可解释运筹学（EOR）框架，强调伴随优化过程的可行动、易理解的解释。EOR核心是决策信息概念，源于“如果-那么”分析，聚焦复杂约束（或参数）变化对决策的影响评估。具体地，采用二部图量化模型变化，并利用大模型增强解释能力。此外，本文引入首个工业级基准，用于严格评估运筹学中解释与分析的有效性，为该领域建立透明与清晰的新标准。
原文摘要 · Abstract (English)
Operations Research (OR) is vital for decision-making in many industries. While recent OR methods have seen significant improvements in automation and efficiency through integrating Large Language Models (LLMs), they still struggle to produce meaningful explanations. This lack of clarity raises concerns about transparency and trustworthiness in OR applications. To address these challenges, we propose a comprehensive framework, Explainable Operations Research (EOR), emphasizing actionable and understandable explanations accompanying optimization. The core of EOR is the concept of Decision Information, which emerges from what-if analysis and focuses on evaluating the impact of complex constraints (or parameters) changes on decision-making. Specifically, we utilize bipartite graphs to quantify the changes in the OR model and adopt LLMs to improve the explanation capabilities. Additionally, we introduce the first industrial benchmark to rigorously evaluate the effectiveness of explanations and analyses in OR, establishing a new standard for transparency and clarity in the field.
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