5 papers
Huge-Scale Assortment Optimization with Customer Choice: A Parallel Primal-Dual Approach
Donghao Zhu, Hanzhang Qin, Ching-pei Lee +3
We study huge-scale assortment optimization problems to maximize expected revenue under customer choice, addressing a fundamental challenge in industries such as transportation, re…
Large-Scale Optimization Model Auto-Formulation: Harnessing LLM Flexibility via Structured Workflow
Kuo Liang, Yuhang Lu, Jianming Mao +7
Large-scale optimization is a key backbone of modern business decision-making. However, building these models is often labor-intensive and time-consuming. We address this by propos…
CogniPair: From LLM Chatbots to Conscious AI Agents -- GNWT-Based Multi-Agent Digital Twins for Social Pairing -- Dating & Hiring Applications
Wanghao Ye, Sihan Chen, Yiting Wang +18
Current large language model (LLM) agents lack authentic human psychological processes necessary for genuine digital twins and social AI applications. To address this limitation, w…
Thompson Sampling for Repeated Newsvendor
Li Chen, Hanzhang Qin, Yunbei Xu +2
In this paper, we investigate the performance of Thompson Sampling (TS) for online learning with censored feedback, focusing primarily on the classic repeated newsvendor model--a f…
On Pareto Optimality for Parametric Choice Bandits
Jierui Zuo, Hanzhang Qin
We study online assortment optimization under stochastic choice when a decision maker simultaneously values cumulative revenue performance and the quality of post-hoc inference on…