Assortment Optimization

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Assortment optimization studies which products to offer, and sometimes where to place them, when customers choose among the offer and an outside option. Methods model choice with multinomial logit, ranking-based, and Markov-chain models, then use combinatorial optimization to handle inventory, capacity, diversity, and placement constraints. Online bandit methods learn customer preferences while balancing exploration and revenue, including settings with contextual choices, pricing, and changing arrivals. A central challenge is making optimization scalable while accounting for imperfect choice models, heterogeneous markets, and the cost of learning through real decisions.

Latest papers

13 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

AI-Mediation in Retail Markets: Revenue Effects and Assortment Optimization
Oct 9, 2026Omar El Housni, Marouane Ibn BrahimAssortment Optimization Discrete Choice Modeling
Cornell Tech, Cornell University
Curating Always-Loaded Context for LLM Agents: A Capacitated Assortment Model with Censored Feedback
Oct 7, 2026Zexuan Liu, Yuning Yang, Tiancheng ZhaoLLM Context Management LLM Agents
University of Illinois at Urbana-Champaign · Georgia Institute of Technology · Saint Louis University
Diffusion-Based Data-Driven Assortment Optimization
Aug 11, 2026Junyi Liao, Xiaohui Jiang, Zhengwei Tong +2Diffusion Model Guidance Combinatorial Optimization
Department of Electrical and Computer Engineering, Duke University · Department of Biostatistics and Bioinformatics, Duke University · Department of Computer Science, Duke University
Diversified Multinomial Logit Contextual Bandits
Jul 13, 2026Heesang Ann, Taehyun Hwang, Min-hwan OhMulti-Armed Bandits Discrete Choice Modeling
Seoul National University
Estimation, Prediction, and Assortment Optimization for Markov Chain Choice Models with Panel Data
Jul 10, 2026Yalcin Akcay, Gerardo Berbeglia, Young-San LinExpectation-Maximization Discrete Choice Modeling
Melbourne Business School
Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides
Jun 9, 2026Rahul Roy, Nur Sunar, Jayashankar M. SwaminathanAssortment Optimization
IE Business School, IE University, Madrid 28006 · Kenan-Flagler Business School, The University of North Carolina at Chapel Hill, NC 27599
Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification
May 25, 2026Joongkyu Lee, Min-hwan OhMulti-Armed Bandits Discrete Choice Modeling
Seoul National University, Seoul, Korea.
Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects
May 17, 2026Xi Chen, Shibo Dai, Jiameng Lyu +1Multi-Armed Bandits Discrete Choice Modeling
Leonard N. Stern School of Business, New York University, New York, NY 10012, USA · Qiuzhen College, Tsinghua University, Beijing 100084, China · Department of Management Science, School of Management, Fudan University, Shanghai, 200433, China +1
Optimal Exploration of New Products under Assortment Decisions
Apr 20, 2026Jackie Baek, Atanas Dinev, Thodoris LykourisMulti-Armed Bandits E-Commerce
Stern School of Business, New York University · Massachusetts Institute of Technology
Transfer Learning for Contextual Joint Assortment-Pricing under Cross-Market Heterogeneity
Mar 18, 2026Elynn Chen, Xi Chen, Yi ZhangCross-Domain Transfer Learning Assortment Optimization
New York University, Stern School of Business · Tsinghua University, School of Economics and Management
Poisson-MNL Bandit: Nearly Optimal Dynamic Joint Assortment and Pricing with Decision-Dependent Customer Arrivals
Feb 18, 2026Junhui Cai, Ran Chen, Qitao Huang +2Assortment Optimization Dynamic Pricing
Department of Information Technology, Analytics, and Operations, University of Notre Dame · Department of Statistics and Data Science, Washington University in St. Louis · Department of Mathematics, Tsinghua University +2
Assortment Planning with Sponsored Products
Feb 9, 2024Shaojie Tang, Shuzhang Cai, Jing Yuan +1Assortment Optimization
Naveen Jindal School of Management, The University of Texas at Dallas · Department of Computer Science and Engineering, The University of North Texas · School of Computer Science and Technology, Soochow University
Combinatorial Inference on the Optimal Assortment in Multinomial Logit Models
Jan 28, 2023Shuting Shen, Xi Chen, Ethan X. Fang +1Statistical Hypothesis Testing Assortment Optimization
Department of Biostatistics, Harvard T.H. Chan School of Public Health · Leonard N. Stern School of Business, New York University · Department of Biostatistics & Bioinformatics, Duke University

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AI-Mediation in Retail Markets: Revenue Effects and Assortment Optimization

Omar El Housni, Marouane Ibn Brahim

Cornell Tech, Cornell University

Assortment Optimization Discrete Choice Modeling

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AI shopping assistants increasingly influence which products consumers consider. We study retail assortment planning under AI mediation, where a retailer chooses a catalog, an AI intermediary forms a customer-facing shortlist, and the customer chooses from that shortlist according to a multinomial logit model. We consider both deterministic mediation, in which the AI follows a fixed segment-specific ranking, and stochastic mediation based on the Plackett-Luce model. Empirically, we construct a dataset of product rankings from leading AI models, estimate Plackett-Luce ranking models, and evaluate their predictive performance on held-out assortments. Combining these estimates with synthetic customer preferences, we find that accounting for AI mediation can materially change optimal assortments and revenue. Across the tested capacity configurations, ignoring mediation yields mean revenue losses between 2.52% and 11.37% relative to the PL optimum. A much simpler deterministic plug-in, based only on the ordering of fitted AI utilities, performs substantially better, with mean losses of at most 0.53%. Motivated by this performance, we study assortment optimization under deterministic mediation. We characterize its computational complexity and show that, for any fixed number of customer segments, the problem admits a fully polynomial-time approximation scheme.