管理评论

管理评论 ›› 2026, Vol. 38 ›› Issue (3): 249-260.

• 运营与供应链管理 • 上一篇    

决策依赖型数据驱动生产优化模型研究

王贵涛1, 毛宇晨1, 刘天奇2, 王曙明1   

  1. 1. 中国科学院大学经济与管理学院, 北京 100190;
    2. 首都经济贸易大学管理工程学院, 北京 100070
  • 收稿日期:2023-10-07 发布日期:2026-04-11
  • 作者简介:王贵涛,中国科学院大学经济与管理学院博士研究生;毛宇晨,中国科学院大学经济与管理学院研究助理,硕士;刘天奇(通讯作者),首都经济贸易大学管理工程学院讲师,博士;王曙明,中国科学院大学经济与管理学院教授,博士生导师,博士。
  • 基金资助:
    国家自然科学基金项目（72171221；72471224；72401206）；首都经济贸易大学新入职青年教师科研启动基金项目（XRZ2024019）。

Data-driven Decision-dependent Production Optimization

Wang Guitao1, Mao Yuchen1, Liu Tianqi2, Wang Shuming1   

  1. 1. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190;
    2. School of Management and Engineering, Capital University of Economics and Business, Beijing 100070
  • Received:2023-10-07 Published:2026-04-11

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摘要： 本文在数据驱动背景下研究了制造企业的产品生产和产能联合优化问题。该问题的一个显著特征是不确定产品需求具有生产决策依赖性。本文首先利用统计学习模型构建产品决策和外生协变量对需求的预测函数。进一步，基于此预测模型构建了决策依赖型数据驱动两阶段随机生产优化模型。此外，在单一生产设施情况下，给出了模型最优产能决策的解析解与最优生产决策的伪多项式时间求解算法；在一般情况下，给出了模型的混合整数线性规划等价形式。最后，通过基于真实数据的数值实验验证了决策依赖性建模与预测模型的嵌入对提高生产决策质量的有效性。

关键词: 生产优化, 两阶段随机规划, 决策依赖, 数据驱动, 需求不确定性

Abstract: This paper investigates the joint optimization issue of product production and capacity for manufacturing enterprises in a data-driven context. A notable feature of this issue is that uncertain product demand depends on production decisions. Firstly, the paper utilizes statistical learning models to construct a prediction function for demand based on product decisions and exogenous covariates. Furthermore, based on this prediction model, a decision-dependent data-driven two-stage stochastic production optimization model is constructed. Additionally, in the case of a single production facility, the paper provides an analytical solution for the optimal capacity decision and a pseudo-polynomial time algorithm for solving the optimal production decision. In general cases, an equivalent reformulation of the model as a mixed-integer linear programming is given. Finally, numerical experiments based on real data validate the significance of incorporating decision-dependency modeling and prediction models in enhancing the quality of production decisions.

Key words: production optimization, two-stage stochastic programming, decision-dependent, data-driven, demand uncertainty