IDEAS home Printed from https://ideas.repec.org/r/eee/ejores/v273y2019i3p1052-1064.html

Assortment optimization under the Sequential Multinomial Logit Model

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Gerardo Berbeglia & Alvaro Flores & Guillermo Gallego, 2021. "The Refined Assortment Optimization Problem," Papers 2102.03043, arXiv.org.
  2. Jacob Feldman & Puping Jiang, 2023. "Display optimization under the multinomial logit choice model: Balancing revenue and customer satisfaction," Production and Operations Management, Production and Operations Management Society, vol. 32(11), pages 3374-3393, November.
  3. Amin Eskandari & Koorush Ziarati & Alireza Nikseresht, 2025. "Bi-objective assortment optimization under a ranking-based choice model: formulation and solution approach using NSGA-II," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(6), pages 568-583, December.
  4. Woonghee Tim Huh & Joseph Paat & Maurice Queyranne, 2026. "Performance of the Offer-Everything Policy," Operations Research, INFORMS, vol. 74(1), pages 141-160, January.
  5. Yunzong Xu & Zizhuo Wang, 2023. "Assortment Optimization for a Multistage Choice Model," Manufacturing & Service Operations Management, INFORMS, vol. 25(5), pages 1748-1764, September.
  6. Shouchang Chen & Zhenzhen Yan & Yun Fong Lim, 2024. "Managing the Personalized Order-Holding Problem in Online Retailing," Manufacturing & Service Operations Management, INFORMS, vol. 26(1), pages 47-65, January.
  7. 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.
  8. Huang, Shihao & Li, Shan & Xie, Hang & Chiu, Chun-Hung, 2025. "Channel merchandising strategies considering customer behavior and supplier encroachment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 193(C).
  9. Chenhao Wang & Yao Wang & Shaojie Tang, 2025. "Advertising meets assortment planning: joint advertising and assortment optimization under multinomial logit model," Journal of Combinatorial Optimization, Springer, vol. 49(2), pages 1-35, March.
  10. 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.
  11. 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.
  12. Ying-Ju Chen & Guillermo Gallego & Pin Gao & Yang Li, 2025. "Position Auctions with Endogenous Product Information: Why Live-Streaming Advertising Is Thriving," Management Science, INFORMS, vol. 71(11), pages 9290-9307, November.
  13. 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.
  14. Pin Gao & Yuhang Ma & Ningyuan Chen & Guillermo Gallego & Anran Li & Paat Rusmevichientong & Huseyin Topaloglu, 2021. "Assortment Optimization and Pricing Under the Multinomial Logit Model with Impatient Customers: Sequential Recommendation and Selection," Operations Research, INFORMS, vol. 69(5), pages 1509-1532, September.
  15. 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.
  16. Chenxu Ke & Ruxian Wang, 2022. "Cross-Category Retailing Management: Substitution and Complementarity," Manufacturing & Service Operations Management, INFORMS, vol. 24(2), pages 1128-1145, March.
  17. Junyu Cao & Wei Sun, 2024. "Tiered Assortment: Optimization and Online Learning," Management Science, INFORMS, vol. 70(8), pages 5481-5501, August.
  18. 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.
  19. Yufeng Cao & Paat Rusmevichientong & Huseyin Topaloglu, 2023. "Revenue Management Under a Mixture of Independent Demand and Multinomial Logit Models," Operations Research, INFORMS, vol. 71(2), pages 603-625, March.
  20. Liu, Jian & Sun, Hailin & Xu, Huifu, 2025. "Bayesian Nash Equilibrium in price competition under multinomial logit demand," European Journal of Operational Research, Elsevier, vol. 324(2), pages 669-689.
  21. 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.
  22. 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.
  23. 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.
  24. Laurent Alfandari & Alborz Hassanzadeh & Ivana Ljubić, 2021. "An Exact Method for Assortment Optimization under the Nested Logit Model," Working Papers hal-02463159, HAL.
  25. Jacob Feldman & Danny Segev, 2022. "Technical Note—The Multinomial Logit Model with Sequential Offerings: Algorithmic Frameworks for Product Recommendation Displays," Operations Research, INFORMS, vol. 70(4), pages 2162-2184, July.
  26. Julia Heger & Robert Klein, 2024. "Assortment optimization: a systematic literature review," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(4), pages 1099-1161, December.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.