可替代产品的联合库存配置、个性化 assortment 与订单履行
Joint Inventory Placement, Assortment Personalization, and Order Fulfillment for Substitutable Products
AI总结：
针对可替代产品的联合库存配置、个性化 assortment 与订单履行问题，该研究开发了具理论保证和强实证性能的近似算法，在多项 logit 模型下获 0.080 至 0.199 的常数因子近似，为该问题提供了首个高效算法。
AI中文摘要：
现代在线零售商利用分布式仓库网络快速履行客户订单。我们研究一个联合决策问题：（i）如何在仓库容量和产品供应约束下，于网络中的仓库间分配库存；（ii）如何根据客户偏好、位置及实时库存水平动态选择个性化产品 assortment；（iii）选择哪个仓库履行客户选定的产品，以最大化期望利润。在我们的模型中，企业在销售周期开始时确定仓库间的库存配置。不同类型的客户按已知分布在有限时间周期内离散到达。当提供 assortment 时，每种客户类型根据离散选择模型最多选择一件产品。随后企业选择可行的仓库履行选定产品，消耗其库存，并获得取决于产品、客户类型和仓库选择的利润。我们开发了具有可证明理论保证和强经验性能的近似算法与策略。在一般概率选择模型下，当仓库容量和产品供应规模扩大时，我们建立了渐近最优近似算法。在多项 logit（MNL）选择模型下，我们获得了常数因子近似算法，近似因子在最一般场景下为 0.080，在平稳性和无限产品供应（即仅存在仓库容量约束）下为 0.199。据我们所知，这些结果为联合库存配置、个性化 assortment 与订单履行问题提供了首个可证明高效的算法。
英文摘要：
Modern online retailers leverage networks of distributed warehouses to rapidly fulfill customer orders. We study a problem of jointly deciding (i) how to allocate inventories across warehouses in the network subject to warehouse capacity and product supply constraints, (ii) how to dynamically select personalized product assortments based on customer preferences and location, as well as real-time stock levels, and (iii) which warehouse to use to fulfill the product chosen by the customer to maximize expected profit. In our model, the firm chooses an inventory placement across warehouses at the start of the selling horizon. Customers of different types then arrive at discrete time periods over a finite time horizon according to a known distribution. When offered an assortment, each customer type chooses at most one product according to a discrete choice model. The firm then chooses a feasible warehouse from which to fulfill the chosen product, depleting its stock and earning a profit that depends on the product, customer type, and warehouse choice. We develop approximation algorithms and policies with provable theoretical guarantees and strong empirical performance. Under general probabilistic choice models, we establish asymptotically optimal approximation algorithms as warehouse capacities and product supplies scale. Under the multinomial logit (MNL) choice model, we obtain constant-factor approximation algorithms, with approximation factors ranging from 0.080 in the most general setting to 0.199 under stationarity and unlimited product supplies, which means that only warehouse capacity constraints are present. To the best of our knowledge, these results provide the first provably efficient algorithms for the joint inventory placement, assortment personalization, and order fulfillment problem.
