发表机构
University of Minnesota; The Dow Chemical Company; Carnegie Mellon University（明尼苏达大学; 陶氏化学公司; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出数据驱动的逆优化框架，推断工业生产规划中专家决策隐含的目标函数，应用于陶氏化学案例证实该方法可将隐性专业知识转化为可解释的决策支持模型。
AI 中文摘要
制造业的生产规划常依赖优化模型，但定义合适的目标函数颇具挑战。实际中，规划者需权衡相互冲突的目标、管理不确定性，并考虑难以量化的定性业务偏好，导致诸多优化模型无法匹配专家行为，限制了其可信度与应用。本研究提出一种数据驱动的逆优化框架，用于推断专家规划者决策中隐含的目标函数。我们将生产规划问题建模为混合整数线性规划，其中未知目标函数表示为假设成本项的加权和，随后应用基于次优损失的逆优化方法从历史生产计划中学习目标权重。该方法应用于陶氏化学提供的真实工业案例，推断出的权重显示，避免库存短缺和维持一致的周期长度是规划者决策的主导因素；针对时间和产品的扩展进一步提升了预测精度，并揭示了不断变化的优先级，专家访谈也证实了这些见解的实际有效性。总体而言，本研究表明逆优化可将隐性人类专业知识转化为可解释模型，为复杂工业系统构建更准确、可信的决策支持工具。
英文摘要
Production planning in the manufacturing industry often relies on the use of optimization models, but defining an appropriate objective function can be a challenge. In practice, planners must balance competing goals, manage uncertainty, and account for qualitative business preferences that are difficult to quantify. As a result, many optimization models fail to match expert behavior, limiting trust and adoption. In this work, we propose a data-driven inverse optimization framework to infer the objective function implicitly captured in expert planners' decisions. We formulate the production planning problem as a mixed-integer linear program, where the unknown objective function is represented as a weighted sum of hypothesized cost terms. A suboptimality-loss-based inverse optimization method is then applied to learn the objective weights from historical production plans. The proposed approach is applied to a real industrial case provided by Dow, where the inferred weights reveal that avoiding inventory shortages and maintaining consistent cycle lengths dominate the planners' decision-making. Time- and product-dependent extensions further improve predictive accuracy and uncover evolving priorities. Expert interviews confirm the practical validity of these insights. Overall, this study shows that inverse optimization can transform tacit human expertise into interpretable models, enabling more accurate and trusted decision-support tools for complex industrial systems.
Journal refChemical Engineering Research and Design, 2026
DOI：10.1016/j.cherd.2026.07.065
