Randomized Assortment Optimization
研究在品类优化中，由于选择模型估计存在统计误差，随机化选择品类而非确定性选择能提升绩效，并展示了如何为常见选择模型设计最优随机化策略。
When a firm selects an assortment of products to offer to customers, it uses a choice model to anticipate their probability of purchasing each product. In practice, the estimation of these models is subject to statistical errors, which may lead to significantly suboptimal assortment decisions. In “Randomized Assortment Optimization,” Wang, Peura, and Wiesemann show that the standard approach of deterministically selecting a single assortment to offer is not always optimal in this setting: Instead, the firm can do better by selecting an assortment randomly according to a prudently designed probability distribution. The authors show how an optimal randomization strategy can be determined for common choice models, improving performance in realistic data-driven settings. The results suggest that more general versions of the assortment optimization problem—incorporating business constraints, more flexible choice models and/or more general forms of uncertainty—tend to be more receptive to the benefits of randomization.