MNL-Prophet: Sequential Assortment Selection under Uncertainty
Vineet Goyal, Salal Humair, Orestis Papadigenopoulos, Assaf Zeevi

TL;DR
This paper introduces online algorithms for sequential assortment selection under uncertainty, achieving optimal revenue performance in certain choice models and extending to constrained scenarios with connections to Prophet Inequalities.
Contribution
It develops threshold-based online policies for sequential assortment selection that are optimal under the Multinomial Logit model and extends to knapsack constraints.
Findings
Policies are worst-case optimal under MNL model.
Extensions to knapsack constraints are provided.
Connections to Prophet Inequality problem are discussed.
Abstract
Due to numerous applications in retail and (online) advertising the problem of assortment selection has been widely studied under many combinations of discrete choice models and feasibility constraints. In many situations, however, an assortment of products has to be constructed gradually and without accurate knowledge of all possible alternatives; in such cases, existing offline approaches become inapplicable. We consider a stochastic variant of the assortment selection problem, where the parameters that determine the revenue and (relative) demand of each item are jointly drawn from some known item-specific distribution. The items are observed sequentially in an arbitrary and unknown order; upon observing the realized parameters of each item, the decision-maker decides irrevocably whether to include it in the constructed assortment, or forfeit it forever. The objective is to maximize…
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Taxonomy
TopicsAuction Theory and Applications · Supply Chain and Inventory Management · Optimization and Search Problems
