1 | Tinghan (Joe) Ye

LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection

Fri, 02 Jan 2026 00:00:00 +0000

Accepted to the 35th International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2026).

We introduce an LLM-powered decision aid that captures qualitative preferences for multi-objective selection.

Paratransit Optimization with Constraint Programming: A Case Study in Savannah, Georgia

Thu, 01 Jan 2026 00:00:00 +0000

Accepted to the 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026).

Conformal Predictive Distributions for Order Fulfillment Time Forecasting

Thu, 22 May 2025 00:00:00 +0000

Accurate estimation of order fulfillment time is critical for e-commerce logistics, yet traditional rule-based approaches often fail to capture the inherent uncertainties in delivery operations. This paper introduces a novel framework for distributional forecasting of order fulfillment time, leveraging Conformal Predictive Systems and Cross Venn-Abers Predictors, model-agnostic techniques that provide rigorous coverage or validity guarantees.

The proposed machine learning methods integrate granular spatiotemporal features, capturing fulfillment location and carrier performance dynamics to enhance predictive accuracy. Additionally, a cost-sensitive decision rule is developed to convert probabilistic forecasts into reliable point predictions. Experimental evaluation on a large-scale industrial dataset demonstrates that the proposed methods generate competitive distributional forecasts, while machine learning-based point predictions significantly outperform the existing rule-based system, achieving up to 14% higher prediction accuracy and up to 75% improvement in identifying late deliveries.

Evaluating Solvers for Linearly Constrained Simulation Optimization

Sun, 15 Dec 2024 00:00:00 +0000

This paper is part of SimOpt, an open-source testbed of simulation-optimization algorithms. I contributed test problems such as a COVID-19 testing frequency problem and an emergency medical volunteer response problem, and I implemented solvers including STRONG and ADAM. This work extends that effort by studying solver performance on problems with linear constraints and by strengthening the benchmark suite available to the community.