This paper provides a tutorial on efficiently solving large-scale nonlinear programs, which includes: (i) guidelines for careful formulation, (ii) the use of initial solutions, (iii) the provision of bounds, (iv) the principles of numerical stability, and (v) the use of the solvers to improve performance.
This paper shows how policy regularizations grounded in classical inventory concepts improve the performance of deep reinforcement learning while reducing hyperparameter tuning effort. Extensive synthetic experiments and deployment on Alibaba’s Tmall platform demonstrate the practical value of incorporating inventory structure into reinforcement learning.
Perishable inventory management remains a critical challenge for online grocery retailers, where mismatches between demand and shelf life can lead to substantial financial losses and food waste. This article describes the development and deployment of an integrated, data-driven system for managing near-expiry (“red-line”) inventory across a nationwide network of microfulfillment centers operated by Meituan’s Xiaoxiang Supermarket. The system jointly optimizes nighttime inventory rebalancing and daytime clearance pricing using a closed-loop learning-and-optimization framework. It combines continuous causal forest to estimate price elasticities, a linearized model for inventory rebalancing, and a Markov decision process for dynamic pricing. Across the full operational network, spoilage rates are about 20% lower and revenue from red-line inventory about 14% higher since deployment, with an annual financial benefit and an annual reduction in the value of food waste each estimated on the order of tens of millions of CNY. This case shows how analytics can be embedded into large-scale retail operations to improve both economic and sustainability outcomes. History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2025 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research.
We develop a scalable optimization system that automates strategic ride pricing at Lyft by solving a novel quasiconvex program balancing financial tradeoffs across 300+ markets. The system replaces manual, ad hoc pricing heuristics with a principled, data-driven framework, delivering a 1% increase in bookings — over $100 million annually — alongside improved analyst productivity and service quality.
The four papers in this special issue were selected as finalists for the 2025 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research. Established in 1998 to commemorate the life and work of Daniel H. Wagner, the prize recognizes work that combines strong analytical content with clear exposition and demonstrated success in practice. Wagner believed strongly in bringing mathematical rigor to practical problems while also emphasizing that analytical solutions should be communicated clearly and intelligibly. The papers in this issue exemplify that ideal. They address very different decisions—from managing e-commerce inventory and perishable goods, to setting rideshare prices, to planning urban cycling infrastructure—yet share a common achievement: They turn sophisticated analytics into practical solutions with measurable impact.
