We address two important concerns faced by assortment managers, namely constrained assortment optimization and assortment personalization. We contribute to addressing these concerns by developing bounds and heuristics based on auxiliary multinomial logit (MNL) models. More precisely, we first...

A central problem in revenue management, known as the assortment problem, consists in deciding which subset of products to offer to consumers in order to maximise revenue. A simple and natural strategy is to select the best assortment out of all those that are constructed by fixing a threshold...

A growing body of research suggests that an abundance of choices can lead to decision-making difficulties for consumers. Rather than maximizing utility, many consumers employ a satisficing decision-making approach, whereby they search for products sequentially until they find one that is...

We study the assortment optimization problem and show that local optima are global optima for all discrete choice models that can be represented by the Markov Chain model. We develop a forward greedy heuristic that finds an optimal assortment for the Markov Chain model and runs in $O(n^2)$...