Omnichannel Assortment Optimization Under the Multinomial Logit Model with a Features Tree
Omnichannel Assortment Optimization Under the Multinomial Logit Model with a Features Tree
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DOI：
10.1287/msom.2021.1001
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Venus Lo;Huseyin Topaloglu
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文献类型：
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作者：
Venus Lo;Huseyin Topaloglu
问题定义：我们考虑经营实体店和在线商店的零售商的品种优化问题。可以提供的产品按其功能进行描述。顾客购买他们喜欢的商店提供的产品。不过，从网上商店购买的顾客可以先试用实体店提供的产品。这些客户根据与店内产品共享的功能来修改他们对在线产品的偏好。在线提供完整的品种，目标是为实体店选择一个品种，以最大限度地提高零售商的总预期收入。学术/实践相关性：实体店的品种会影响对在线产品的偏好。与传统的品类优化不同，实体店的品类会影响两家商店的收入。方法论：我们引入了功能树来按功能组织产品。树上的非叶顶点对应于特征，叶顶点对应于产品。叶子的祖先对应于产品的特征。顾客根据多项 Logit 模型在商店品种中选择产品。我们考虑两种设置；要么所有客户在实体店查看产品后在线购买，要么我们有混合的客户从每个商店购买。结果：当所有顾客在线购买时，我们提供了一种有效的算法来找到在实体店中展示的最佳品种。对于混合客户，问题变得 NP 困难，我们给出了完全多项式时间近似方案。我们通过数值证明，我们可以非常接近产品具有任意特征组合而无需树结构的情况，并且我们的完全多项式时间近似方案表现得非常好。管理意义：我们描述了最佳展示具有被低估功能的昂贵产品和暴露具有被高估功能的廉价产品的最佳条件。
Problem definition: We consider the assortment optimization problem of a retailer that operates a physical store and an online store. The products that can be offered are described by their features. Customers purchase among the products that are offered in their preferred store. However, customers who purchase from the online store can first test out products offered in the physical store. These customers revise their preferences for online products based on the features that are shared with the in-store products. The full assortment is offered online, and the goal is to select an assortment for the physical store to maximize the retailer’s total expected revenue. Academic/practical relevance: The physical store’s assortment affects preferences for online products. Unlike traditional assortment optimization, the physical store’s assortment influences revenue from both stores. Methodology: We introduce a features tree to organize products by features. The nonleaf vertices on the tree correspond to features, and the leaf vertices correspond to products. The ancestors of a leaf correspond to features of the product. Customers choose among the products within their store’s assortment according to the multinomial logit model. We consider two settings; either all customers purchase online after viewing products in the physical store, or we have a mix of customers purchasing from each store. Results: When all customers purchase online, we give an efficient algorithm to find the optimal assortment to display in the physical store. With a mix of customers, the problem becomes NP-hard, and we give a fully polynomial-time approximation scheme. We numerically demonstrate that we can closely approximate the case where products have arbitrary combinations of features without a tree structure and that our fully polynomial-time approximation scheme performs remarkably well. Managerial implications: We characterize conditions under which it is optimal to display expensive products with underrated features and expose inexpensive products with overrated features.