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Assortment optimization under the Sequential Multinomial Logit Model

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  • Flores, Alvaro
  • Berbeglia, Gerardo
  • Van Hentenryck, Pascal

Abstract

We study the assortment optimization problem under the Sequential Multinomial Logit (SML), a discrete choice model that generalizes the Multinomial Logit (MNL). Under the SML model, products are partitioned into two levels, to capture differences in attractiveness, brand awareness and, or visibility of the products in the market. When a consumer is presented with an assortment of products, she first considers products in the first level and, if none of them is purchased, products in the second level are considered. This model is a special case of the Perception-Adjusted Luce Model (PALM) recently proposed by Echenique et al. (2018). It can explain many behavioral phenomena such as the attraction, compromise, similarity effects and choice overload which cannot be explained by the MNL model or any discrete choice model based on random utility. In particular, the SML model allows violations to regularity which states that the probability of choosing a product cannot increase if the offer set is enlarged.

Suggested Citation

  • Flores, Alvaro & Berbeglia, Gerardo & Van Hentenryck, Pascal, 2019. "Assortment optimization under the Sequential Multinomial Logit Model," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1052-1064.
    • Handle: RePEc:eee:ejores:v:273:y:2019:i:3:p:1052-1064
    DOI: 10.1016/j.ejor.2018.08.047
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    Cited by:

    1. Gerardo Berbeglia & Alvaro Flores & Guillermo Gallego, 2021. "The Refined Assortment Optimization Problem," Papers 2102.03043, arXiv.org.
    2. Mika Sumida & Guillermo Gallego & Paat Rusmevichientong & Huseyin Topaloglu & James Davis, 2021. "Revenue-Utility Tradeoff in Assortment Optimization Under the Multinomial Logit Model with Totally Unimodular Constraints," Management Science, INFORMS, vol. 67(5), pages 2845-2869, May.
    3. Nan Liu & Yuhang Ma & Huseyin Topaloglu, 2020. "Assortment Optimization Under the Multinomial Logit Model with Sequential Offerings," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 835-853, July.
    4. Bobokhujaev B. N., 2020. "Product Assortment Policy in Business Entities: Tactics and Strategies," International Journal of Innovation and Economic Development, Inovatus Services Ltd., vol. 6(2), pages 55-60, June.
    5. Page, Kenneth & Pérez, Juan & Telha, Claudio & García-Echalar, Andrés & López-Ospina, Héctor, 2021. "Optimal bundle composition in competition for continuous attributes," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1168-1187.
    6. Uzma Mushtaque & Jennifer A. Pazour, 2022. "Assortment optimization under cardinality effects and novelty for unequal profit margin items," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(1), pages 106-126, February.
    7. Alfandari, Laurent & Hassanzadeh, Alborz & Ljubic, Ivana, 2020. "An Exact Method for Assortment Optimization under the Nested Logit Model," ESSEC Working Papers WP2001, ESSEC Research Center, ESSEC Business School, revised 2020.
    8. Mehrani, Saharnaz & Sefair, Jorge A., 2022. "Robust assortment optimization under sequential product unavailability," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1027-1043.
    9. 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.
    10. Timonina-Farkas, Anna & Katsifou, Argyro & Seifert, Ralf W., 2020. "Product assortment and space allocation strategies to attract loyal and non-loyal customers," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1058-1076.
    11. Laurent Alfandari & Alborz Hassanzadeh & Ivana Ljubić, 2021. "An Exact Method for Assortment Optimization under the Nested Logit Model," Working Papers hal-02463159, HAL.

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