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Optimal Dynamic Assortment Planning with Demand Learning

Author

Listed:
  • Denis Sauré

    (Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260)

  • Assaf Zeevi

    (Graduate School of Business, Columbia University, New York, New York 10027)

Abstract

We study a family of stylized assortment planning problems, where arriving customers make purchase decisions among offered products based on maximizing their utility. Given limited display capacity and no a priori information on consumers' utility, the retailer must select which subset of products to offer. By offering different assortments and observing the resulting purchase behavior, the retailer learns about consumer preferences, but this experimentation should be balanced with the goal of maximizing revenues. We develop a family of dynamic policies that judiciously balance the aforementioned trade-off between exploration and exploitation, and prove that their performance cannot be improved upon in a precise mathematical sense. One salient feature of these policies is that they “quickly” recognize, and hence limit experimentation on, strictly suboptimal products.

Suggested Citation

  • Denis Sauré & Assaf Zeevi, 2013. "Optimal Dynamic Assortment Planning with Demand Learning," Manufacturing & Service Operations Management, INFORMS, vol. 15(3), pages 387-404, July.
    • Handle: RePEc:inm:ormsom:v:15:y:2013:i:3:p:387-404
    DOI: 10.1287/msom.2013.0429
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    References listed on IDEAS

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    3. Gur, Yonatan & Macnamara, Gregory & Saban, Daniela, 2020. "On the Disclosure of Promotion Value in Platforms with Learning Sellers," Research Papers 3865, Stanford University, Graduate School of Business.
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    6. Yining Wang & Xi Chen & Xiangyu Chang & Dongdong Ge, 2021. "Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1703-1717, June.
    7. Agrawal, Priyank & Tulabandhula, Theja & Avadhanula, Vashist, 2023. "A tractable online learning algorithm for the multinomial logit contextual bandit," European Journal of Operational Research, Elsevier, vol. 310(2), pages 737-750.
    8. Tai-Yu Ma & Sylvain Klein, 2020. "Integrated ridesharing services with chance-constrained dynamic pricing and demand learning," Papers 2001.09151, arXiv.org, revised Jun 2020.
    9. Xi Chen & Yining Wang & Yuan Zhou, 2018. "Dynamic Assortment Optimization with Changing Contextual Information," Papers 1810.13069, arXiv.org, revised Jan 2019.
    10. Elçin Ergin & Mehmet Gümüş & Nathan Yang, 2022. "An Empirical Analysis of Intra‐Firm Product Substitutability in Fashion Retailing," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 607-621, February.
    11. Gel, Esma S. & Salman, F. Sibel, 2022. "Dynamic ordering decisions with approximate learning of supply yield uncertainty," International Journal of Production Economics, Elsevier, vol. 243(C).
    12. Shaojie Tang & Jing Yuan, 2021. "Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2143-2161, July.
    13. Yonatan Gur & Gregory Macnamara & Ilan Morgenstern & Daniela Saban, 2019. "Information Disclosure and Promotion Policy Design for Platforms," Papers 1911.09256, arXiv.org, revised Dec 2022.
    14. Omar Besbes & Yonatan Gur & Assaf Zeevi, 2016. "Optimization in Online Content Recommendation Services: Beyond Click-Through Rates," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 15-33, February.
    15. Claudio Cardoso Flores & Marcelo Cunha Medeiros, 2020. "Online Action Learning in High Dimensions: A Conservative Perspective," Papers 2009.13961, arXiv.org, revised Mar 2024.
    16. Fernando Bernstein & A. Gürhan Kök & Lei Xie, 2015. "Dynamic Assortment Customization with Limited Inventories," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 538-553, October.
    17. Francetich, Alejandro & Kreps, David, 2020. "Choosing a good toolkit, II: Bayes-rule based heuristics," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    18. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
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    20. Dipankar Das, 2023. "A Model of Competitive Assortment Planning Algorithm," Papers 2307.09479, arXiv.org.

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