Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

Rusmevichientong, Paat; Shen, Zuo‐Jun Max; Shmoys, David B.

doi:10.1287/opre.1100.0866

Cited by 407 publications

(243 citation statements)

References 51 publications

Supporting

Mentioning

239

Contrasting

Order By: Relevance

“…Rusmevichientong and Topaloglu (2011) study the robust assortment problem under the multinomial logit model when some of the parameters of the choice model are not known. Rusmevichientong, Shen and Shmoys (2010) consider constraints on the size of the offered assortment when customers choose according to the multinomial logit model. Kok and Xu (2011) consider joint assortment optimization and pricing problems under the nested logit model, where both the set of products offered and their corresponding prices are decision variables.…”

mentioning

confidence: 99%

Assortment Optimization Under Variants of the Nested Logit Model

Davis

Gallego

Topaloğlu

2014

Operations Research

273

149

View full text Add to dashboard Cite

We study a class of assortment optimization problems where customers choose among the offered products according to the nested logit model. There is a fixed revenue associated with each product. The objective is to find an assortment of products to offer so as to maximize the expected revenue per customer. We show that the problem is polynomially solvable when the nest dissimilarity parameters of the choice model are less than one and the customers always make a purchase within the selected nest. Relaxing either of these assumptions renders the problem NP-hard. To deal with the NP-hard cases, we develop parsimonious collections of candidate assortments with worst-case performance guarantees. We also formulate a convex program whose optimal objective value is an upper bound on the optimal expected revenue. Thus, we can compare the expected revenue provided by an assortment with the upper bound on the optimal expected revenue to get a feel for the optimality gap of the assortment. By using this approach, our computational experiments test the performance of the parsimonious collections of candidate assortments that we develop.

show abstract

mentioning

confidence: 99%

Assortment Optimization Under Variants of the Nested Logit Model

Davis

Gallego

Topaloğlu

2014

Operations Research

273

149

View full text Add to dashboard Cite

show abstract

“…Bernstein et al (2015) and Golrezaei et al (2014) explore how a retailer can best offer dynamic, customized assortments given a customer's type and remaining inventory levels. Several papers have studied how a retailer can learn consumer demand by dynamically changing assortments (Ulu et al 2012, Rusmevichientong et al 2010, Sauré and Zeevi 2013, Caro and Gallien 2007, Farias and Madan 2011. There is also a vast literature on new product introduction timing which can influence a firm's assortment rotation strategy; however, technological advances are the main cause for the assortment rotations studied in most of this work (see, e.g., Ramachandran andKrishnan 2008, Krankel et al 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

Assortment Rotation and the Value of Concealment

Ferreira¹,

Goh

2016

SSRN Journal

View full text Add to dashboard Cite

Assortment rotation -the retailing practice of changing the assortment of products offered to customers -has recently been used as a competitive advantage for both brick-and-mortar and online retailers. Fast-fashion retailers have differentiated themselves by rotating their assortment multiple times throughout a standard selling season. Interestingly, the entire online flash sales industry was created using this idea as a cornerstone of its business strategy. In this paper we identify and investigate a new reason why frequent assortment rotations can be valuable to a retailer, particularly for products where consumers typically purchase multiple products in a given category during a selling season. Namely, by distributing its seasonal catalog of products over multiple assortments rotated throughout the season -as opposed to selling all products in a single assortment -the retailer effectively conceals a portion of its full product catalog from consumers. This injects uncertainty into the consumer's relative product valuations since she is unable to observe the entire catalog of products that the retailer will sell that season. Rationally-acting consumers may respond to this additional uncertainty by purchasing more products, thereby generating additional sales for the retailer. We refer to this phenomenon as the value of concealment. A negative value of concealment is possible and represents the event that rationally-acting consumers respond to the additional uncertainty by purchasing fewer products.We develop a consumer choice model and finite-horizon stochastic dynamic program to study when the value of concealment is positive or negative. We show that when consumers are myopic, the value of concealment is always positive. In contrast, we show that when consumers are strategic, the value of concealment is context-dependent; we present insights and discuss intuition regarding which product categories likely lead to positive vs. negative values of concealment.

show abstract

“…Therefore, a policy that has a number of periods of exploration followed by periods of exploitation such as the "adaptive assortment" of Rusmevichientong et al (2010) and the "separation-based policy" of Saure and Zeevi (2011) is not necessarily optimal in our setting. (Saure and Zeevi 2011 also study the performance of "refined dynamic assortment policy," which may alternate between exploration and exploitation.)…”

Section: Exploration Vs Exploitationmentioning

confidence: 99%

“…Rusmevichientong et al (2010) construct an algorithm for optimizing dynamic assortments under a multinomial logit choice framework, and explore demand learning issues. The last two papers take an adaptive learning approach, whereas Caro and Gallien (2007) take a Bayesian learning approach, as our paper does.…”

mentioning

confidence: 99%

Learning Consumer Tastes Through Dynamic Assortments

Ulu

Honhon

Alptekinoğlu

2012

Operations Research

View full text Add to dashboard Cite

How should a firm modify its product assortment over time when learning about consumer tastes? In this paper, we study dynamic assortment decisions in a horizontally differentiated product category for which consumers' diverse tastes can be represented as locations on a Hotelling line. We presume that the firm knows all possible consumer locations, comprising a finite set, but does not know their probability distribution. We model this problem as a discrete-time dynamic program; each period, the firm chooses an assortment and sets prices to maximize the total expected profit over a finite horizon, given its subjective beliefs over consumer tastes. The consumers then choose a product from the assortment that maximizes their own utility. The firm observes sales, which provide censored information on consumer tastes, and it updates beliefs in a Bayesian fashion. There is a recurring trade-off between the immediate profits from sales in the current period (exploitation) and the informational gains to be exploited in all future periods (exploration). We show that one can (partially) order assortments based on their information content and that in any given period the optimal assortment cannot be less informative than the myopically optimal assortment. This result is akin to the well-known "stock more" result in censored newsvendor problems with the newsvendor learning about demand through sales when lost sales are not observable. We demonstrate that it can be optimal for the firm to alternate between exploration and exploitation, and even offer assortments that lead to losses in the current period in order to gain information on consumer tastes. We also develop a Bayesian conjugate model that reduces the state space of the dynamic program and study value of learning using this conjugate model.

show abstract

Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

Cited by 407 publications

References 51 publications

Assortment Optimization Under Variants of the Nested Logit Model

Assortment Optimization Under Variants of the Nested Logit Model

Assortment Rotation and the Value of Concealment

Learning Consumer Tastes Through Dynamic Assortments

Contact Info

Product

Resources

About