Assortment Optimization Under the Multinomial Logit Model with Sequential Offerings

Nan Liu (), Yuhang Ma () and Huseyin Topaloglu ()
Additional contact information
Nan Liu: Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467
Yuhang Ma: School of Operations Research and Information Engineering, Cornell Tech, New York, New York 10044
Huseyin Topaloglu: School of Operations Research and Information Engineering, Cornell Tech, New York, New York 10044

INFORMS Journal on Computing, 2020, vol. 32, issue 3, 835-853

Abstract: We consider assortment optimization problems, where the choice process of a customer takes place in multiple stages. There is a finite number of stages. In each stage, we offer an assortment of products that does not overlap with the assortments offered in the earlier stages. If the customer makes a purchase within the offered assortment, then the customer leaves the system with the purchase. Otherwise, the customer proceeds to the next stage, where we offer another assortment. If the customer reaches the end of the last stage without a purchase, then the customer leaves the system without a purchase. The choice of the customer in each stage is governed by a multinomial logit model. The goal is to find an assortment to offer in each stage to maximize the expected revenue obtained from a customer. For this assortment optimization problem, it turns out that the union of the optimal assortments to offer in each stage is nested by revenue in the sense that this union includes a certain number of products with the largest revenues. However, it is still difficult to figure out the stage in which a certain product should be offered. In particular, the problem of finding an assortment to offer in each stage to maximize the expected revenue obtained from a customer is NP hard. We give a fully polynomial time approximation scheme for the problem when the number of stages is fixed.

Keywords: marketing; choice models; multinomial logit model; approximation schemes; dynamic programming (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
https://doi.org/10.1287/ijoc.2019.0910 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:32:y:3:i:2020:p:835-853

Access Statistics for this article

More articles in INFORMS Journal on Computing from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().