Using Neural Networks to Guide Data-Driven Operational Decisions
提出用深度神经网络解决数据驱动的随机优化问题，通过训练网络预测目标函数并利用梯度优化决策，在报童、定价和呼叫中心问题中优于现有方法。
We propose deep neural networks for data-driven stochastic optimization. Using historical data (covariates, decisions, costs), we propose to train a neural network to predict the objective value as a function of both the decision and covariate. After training, for a given covariate, this predicted objective is optimized over the decision variables using gradient-based methods with analytical gradients and Hessians. Performance is characterized by neural network generalization bounds. Comprehensive experiments on newsvendor, personalized assortment pricing, and call center staffing problems demonstrate our method’s strength over existing approaches such as conditional stochastic optimization and analytical approximations, especially when (i) the objective function is unknown, (ii) moderate to large data sets are available, or (iii) the problem structure resists simple parametric approximations. This paper was accepted by Chung Piaw Teo, optimization and decision analytics. Funding: The research of N. Chen is supported by the UTMM MARC Grant and the IMI Research Grant. The research of J. Milner is supported by NSERC [Grant 453954]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04141 .