Technical Note—New Bounds for Cardinality-Constrained Assortment Optimization Under the Nested Logit Model
针对嵌套Logit模型下每个类别内产品数量受限的品类优化问题，提出新方法，实验显示平均最优性差距低于1%。
Assortment optimization involves determining the optimal set of products to show customers and is a fundamental problem in retail operations. The nested logit choice model is a popular and widely used choice model to capture customer behavior. In “New Bounds for Cardinality-Constrained Assortment Optimization Under the Nested Logit Model,” Kunnumkal presents a new method for making the assortment decisions under the nested logit choice model when there is a constraint on the number of products that can be offered within each nest. Computational experiments reveal that the assortments obtained by the solution method are near optimal, with the average optimality gap being under 1%.