Using Neural Networks to Guide Data-Driven Operational Decisions

• 成果类型: Article; Early Access
• 署名作者: Lagzi, Saman; Chen, Ningyuan; Milner, Joseph
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of Toronto; University Toronto Mississauga
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.04141
• 发表日期: 2026
• 关键词: Deep Neural Networks; data-driven; Newsvendor Problem; assortment pricing; staffing

摘要

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.