Operations Research
Convex Optimization
Mathematics of Operations Research
Nonlinear Optimization
Intertemporal Pricing with Strategic Customer Behavior
Management Science
Strategic Capacity Rationing to Induce Early Purchases
Management Science
On the Choice-Based Linear Programming Model for Network Revenue Management
Manufacturing & Service Operations Management
Computing Virtual Nesting Controls for Network Revenue Management Under Customer Choice Behavior
Manufacturing & Service Operations Management
The Role of Robust Optimization in Single-Leg Airline Revenue Management
Management Science
A Column Generation Algorithm for Choice-Based Network Revenue Management
Operations Research
A Branch-and-Cut algorithm for graph coloring
Discrete Applied Mathematics - Special issue: IV ALIO/EURO workshop on applied combinatorial optimization
Robust Controls for Network Revenue Management
Manufacturing & Service Operations Management
OM Practice---Choice-Based Revenue Management: An Empirical Study of Estimation and Optimization
Manufacturing & Service Operations Management
Intertemporal Pricing and Consumer Stockpiling
Operations Research
An Improved Dynamic Programming Decomposition Approach for Network Revenue Management
Manufacturing & Service Operations Management
Computing Bid Prices for Revenue Management Under Customer Choice Behavior
Manufacturing & Service Operations Management
Robust solutions of uncertain linear programs
Operations Research Letters
Robust linear optimization under general norms
Operations Research Letters
Estimating Primary Demand for Substitutable Products from Sales Transaction Data
Operations Research
A Nonparametric Approach to Modeling Choice with Limited Data
Management Science
A Nonparametric Approach to Modeling Choice with Limited Data
Management Science
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We study robust formulations of assortment optimization problems under the multinomial logit choice model. The novel aspect of our formulations is that the true parameters of the logit model are assumed to be unknown, and we represent the set of likely parameter values by a compact uncertainty set. The objective is to find an assortment that maximizes the worst-case expected revenue over all parameter values in the uncertainty set. We consider both static and dynamic settings. The static setting ignores inventory consideration, whereas in the dynamic setting, there is a limited initial inventory that must be allocated over time. We give a complete characterization of the optimal policy in both settings, show that it can be computed efficiently, and derive operational insights. We also propose a family of uncertainty sets that enables the decision maker to control the trade-off between increasing the average revenue and protecting against the worst-case scenario. Numerical experiments show that our robust approach, combined with our proposed family of uncertainty sets, is especially beneficial when there is significant uncertainty in the parameter values. When compared to other methods, our robust approach yields over 10% improvement in the worst-case performance, but it can also maintain comparable average revenue if average revenue is the performance measure of interest.