Rapid growth in the world of e-commerce is forcing traditional retailers to rethink their operational efficiency and revenue/cost streams. Retailers are facing increasing pressure to better utilize in-store inventory and reduce end-of-season markdowns. Transshipments between stores can re-balance inventory levels while preventing shortages and backorders. However, the joint inventory and transshipment problem is difficult to optimize in an extensive network, and many retailers lack the capital and managerial bandwidth required to create a system capable of transshipments between any two stores in a network of hundreds. We consider two multi-location newsvendor problems with reactive and proactive transshipments, respectively. We propose clustering stores into transshipment groups while considering both demand correlation and physical distance between locations. Since minimizing demand correlation does not follow the triangle inequality, we introduce two modified clustering algorithms that outperform existing algorithms for distance- and correlation-based clustering. For a given set of clusters, we mathematically model these two problems considering total system profit. In addition, we quantify the performance of distance-based and correlation-based clustering under several inventory ordering and transshipment policies to show the robustness of our results. To verify our methods, we test the proposed methodology using grocery sales data from IRi over three years in Houston and Dallas, TX and liquor licensee purchase information over three years from the Iowa state government. We present correlation, transshipment, and profitability analysis. We show that when demand correlation is present and stable over time, clustering based on demand correlation can maintain high transshipments and profits while decreasing solution complexity.

电子商务世界的快速增长迫使传统零售商重新考虑其运营效率和收入/成本流。零售商面临着越来越大的压力，需要更好地利用店内库存并减少季末降价。商店之间的转运可以重新平衡库存水平，同时防止短缺和缺货。然而，联合库存和转运问题很难在广泛的网络中优化，并且许多零售商缺乏创建能够在数百个网络中的任意两家商店之间转运的系统所需的资金和管理带宽。我们分别考虑了被动转运和主动转运的两个多地点报童问题。我们建议将商店分为转运组，同时考虑需求相关性和地点之间的物理距离。由于最小化需求相关性并不遵循三角不等式，因此我们引入了两种改进的聚类算法，其性能优于基于距离和相关性的聚类的现有算法。对于给定的一组集群，我们考虑系统总利润对这两个问题进行数学建模。此外，我们还量化了几种库存订购和转运策略下基于距离和基于相关性的聚类的性能，以显示我们结果的稳健性。为了验证我们的方法，我们使用 IRi 在休斯顿和达拉斯三年来的杂货销售数据测试了所提出的方法，德克萨斯州和酒类许可证持有者在三年内从爱荷华州政府购买信息。我们提供相关性、转运和盈利能力分析。我们表明，当需求相关性存在并随着时间的推移保持稳定时，基于需求相关性的聚类可以保持高转运量和利润，同时降低解决方案的复杂性。