Online Joint Assortment-Inventory Optimization Under MNL Choices

• 成果类型: Article
• 署名作者: Liang, Yong; Mao, Xiaojie; Wang, Shiyuan
• 署名单位: Tsinghua University; Tsinghua University; Shanghai University of Finance & Economics
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2023.0167
• 发表日期: 2026
• 关键词: assortment planning; Inventory management; multiarmed bandit; multinomial logit; online optimization; upper confidence bound; Multinomial logit model; dynamic assortment; censored newsvendor; optimal acquisition; planning problem; management; algorithms; regret

摘要

We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the multinomial logit (MNL) choice model, and the attraction parameters are unknown a priori. The retailer makes periodic assortment and inventory decisions to dynamically learn from the customer choice observations about the attraction parameters while maximizing the expected total profit over time. In this paper, we propose a novel algorithm that can effectively balance exploration and exploitation in the online decision making of assortment and inventory. Our algorithm builds on a new estimator for the MNL attraction parameters, an innovative approach to incentivize exploration by adaptively tuning certain known and unknown parameters, and an optimization oracle to static single-cycle assortment-inventory planning problems with given parameters. We establish a regret upper bound for our algorithm and a lower bound for the online joint assortment-inventory optimization problem, suggesting that our algorithm achieves nearly optimal regret rate, provided that the static optimization oracle is exact. Then we incorporate more practical approximate static optimization oracles into our algorithm and bound from above the impact of static optimization errors on the regret of our algorithm. We perform numerical studies to demonstrate the effectiveness of our proposed algorithm. At last, we extend our study by incorporating inventory carryover and the learning of customer arrival distribution.