2 | Tinghan (Joe) Ye

Contextual Stochastic Optimization for Omnichannel Multicourier Order Fulfillment under Delivery Time Uncertainty

Tue, 14 Oct 2025 00:00:00 +0000

The paper studies a large-scale order fulfillment problem for a leading e-commerce company in the United States. The challenge involves selecting fulfillment centers and shipping carriers with observational data only to efficiently process orders from a vast network of physical stores and warehouses. The company’s current practice relies on heuristic rules that choose the cheapest fulfillment and shipping options for each unit, without considering opportunities for batching items or the reliability of carriers in meeting expected delivery dates.

The paper develops a data-driven contextual stochastic optimization framework that integrates distributional forecasts of delivery time deviations with stochastic and robust order fulfillment optimization models. The framework optimizes the selection of fulfillment centers and carriers, accounting for item consolidation and delivery time uncertainty. Validated on a real-world dataset containing tens of thousands of products, each with hundreds of fulfillment options, the proposed framework significantly enhances the accuracy of meeting customer-expected delivery dates compared to current practices. It provides a flexible balance between reducing fulfillment costs and managing delivery time deviation risks, emphasizing the importance of contextual information and distributional forecasts in order fulfillment.

Recognition: Honorable Mention, M&SOM Practice-based Research Competition, 2025.

Boosting Column Generation with Graph Neural Networks for Joint Rider Trip Planning and Crew Shift Scheduling

Wed, 01 Oct 2025 00:00:00 +0000

Optimizing service schedules is pivotal to reliable, efficient, and inclusive on-demand mobility. This challenge is especially acute for complex paratransit systems that must jointly coordinate rider trip planning and crew scheduling under tight real-time constraints.

This work develops a graph-neural-network-informed column generation approach for the Joint Rider Trip Planning and Crew Shift Scheduling Problem. The key idea is to reduce the number of paths explored in the pricing problem, accelerating the most time-consuming part of column generation while preserving solution quality. The method was evaluated on a real-world dataset from the paratransit system of Chatham County in Georgia and produced substantial improvements over baseline approaches. Additional project context is available on the SAM lab site.

Cornell University Uses Integer Programming to Optimize Final Exam Scheduling

Tue, 30 Sep 2025 00:00:00 +0000

This paper presents an integer programming framework designed to effectively address the complex final exam scheduling challenges encountered at Cornell University. With a high degree of flexibility, the framework is specifically tailored to accommodate diverse constraints, including the front-loading of large courses and the exclusion of specific time slots during the exam period.

By generating multiple scheduling model variants and incorporating heuristic approaches, the framework enables comprehensive comparisons of different schedules. This empowers the University Registrar to make informed decisions, considering trade-offs in schedule comfort measured by different levels of exam conflicts. The results demonstrate significant time and effort savings for the university administration while enhancing student and faculty satisfaction.

Recognition: Finalist, INFORMS Undergraduate Operations Research Prize, 2023.

Managed Residential Electric Vehicle Charging Minimizes Electricity Bills while Meeting Driver and Community Preferences

Mon, 01 Apr 2024 00:00:00 +0000

Transitions to electric vehicles are expected to increase electricity use in residences, where most drivers tend to recharge. We develop a mathematical programming framework for shifting residential EV charging during low electricity pricing hours to minimize the additional electricity costs that a household incurs from charging its vehicle. The model also accounts for household and community preferences through four secondary objectives: charging as soon as possible on arrival, charging as late as possible before departure, charging for valley filling and peak shaving of residential load, and charging in a shared community hub by using a fast charging station.

We analyze granular residential energy data from a sample of Austin households in 2018 and compare electricity bills under four pricing schemes, including flat rates and time-of-use rates, both with and without a separate meter for EV charging. The findings show that all four secondary charging objectives avoid on-peak charging periods and reduce households’ overall daily electricity costs while surfacing trade-offs in flexibility, charger utilization, and grid load smoothing.

Recognition: 2nd Place, INFORMS Mini Poster Competition, 2021.

A Min-Max Theorem for the Minimum Fleet-Size Problem

Mon, 01 May 2023 00:00:00 +0000

A taxi routing problem can be solved via bipartite matching, where a maximum cardinality matching corresponds to the minimum number of taxis needed to cover all trips. We prove a min-max theorem: the maximum number of pairwise incompatible trips equals the minimum number of taxis needed. We also demonstrate the problem on an NYC taxi dataset and obtain a 35% reduction in the total number of taxis needed.

Part of the work is integrated into a lab for ENGRI 1101: Engineering Applications of OR.