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 .
Lagzi et al. (Mon,) studied this question.