Deep Learning-Driven Contextual Stochastic Optimization for Real-Time Order Fulfillment
Published in NeurIPS 2025 Workshop on ML×OR, 2025
Tinghan Ye, Shuaicheng Tong, Changkun Guan, Beste Basciftci, Pascal Van Hentenryck. "Deep Learning-Driven Contextual Stochastic Optimization for Real-Time Order Fulfillment." NeurIPS 2025 Workshop on ML×OR: Mathematical Foundations and Operational Integration of Machine Learning for Uncertainty-Aware Decision-Making, 2025.
Order fulfillment is a fundamental challenge in large-scale e-commerce, requiring real-time decisions for every incoming order. This work introduces a two-stage contextual stochastic optimization framework that captures delivery timeliness and future inventory consumption uncertainty, and develops deep neural networks to approximate its solutions for real-time deployment during peak hours, when traditional solvers are computationally prohibitive.