Choice-based demand estimation is a fundamental task in retail operations and revenue
management, providing necessary input data for inventory control, assortment, and price …

Abstract Background Discrete choice models (DCMs) for moral choice analysis will likely
lead to erroneous model outcomes and misguided policy recommendations, as only some …

Customer behavior is often assumed to follow weak rationality, which implies that adding a
product to an assortment will not increase the choice probability of another product in that …

Motivated by the successes of deep learning, we propose a class of neural network-based
discrete choice models, called RUMnets, inspired by the random utility maximization (RUM) …

Problem definition. In retailing, discrete choice models (DCMs) are commonly used to
capture the choice behavior of customers when offered an assortment of products. When …

We study the problem of finding the optimal assortment that maximizes expected revenue
under the decision forest model, a recently proposed nonparametric choice model that is …

We study a class of robust assortment optimization problems that was proposed by Farias et
al.[Farias VF, Jagabathula S, Shah D (2013) A nonparametric approach to modeling choice …

Understanding how consumers make choices is of paramount importance, as it offers
insights into consumer purchase behavior across multiple products, enables accurate …

The random utility maximization model is by far the most adopted framework to estimate
consumer choice behavior. However, behavioral economics has provided strong empirical …

Models of choice are a fundamental input to many now-canonical optimization problems in
the field of Operations Management, including assortment, inventory, and price optimization …