Assortment Optimization Under the Multinomial Logit Model with Sequential Offerings

Author:

Liu Nan1ORCID,Ma Yuhang2ORCID,Topaloglu Huseyin2ORCID

Affiliation:

1. Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467;

2. School of Operations Research and Information Engineering, Cornell Tech, New York, New York 10044

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.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Computer Science Applications,Information Systems,Software

Reference23 articles.

1. Aouad A, Farias V, Levi R (2016) Assortment optimization under consider-then-choose choice models. Technical report, Massachusetts Institute of Technology, Cambridge.

2. A Markov Chain Approximation to Choice Modeling

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4. Davis J, Gallego G, Topaloglu H (2013) Assortment planning under the multinomial logit model with totally unimodular constraint structures. Technical report, Cornell University School of Operations Research and Information Engineering, Ithaca, NY.

5. Assortment Optimization Under Variants of the Nested Logit Model

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