Abstract
Robust Dynamic Assortment Optimization Assortment optimization with choice model estimation and learning has been studied extensively in the data-driven revenue management literature. Existing methods and analysis, however, do not take into consideration the fact that some customers arriving at certain time periods might exhibit outlier purchasing behaviors. The work of Chen et al. studies dynamic assortment optimization in the presence of outlier customers modeled by an [Formula: see text]-contamnination model. The impact of outlier customers on the revenue performance of an algorithm is analyzed and discussed.
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