The evolution of the retail business presents new challenges and raises pivotal questions on how to reinvent stores and supply chains to meet the growing demand of the online channel. One of the recent measures adopted by omnichannel retailers is to address the growth of online sales using in-store picking, which allows serving online orders using existing assets. However, it comes with the downside of harming the offline customer experience. To achieve picking policies adapted to the dynamic customer flows of a retail store, we formalize a new problem called Dynamic In-store Picker Routing Problem (diPRP). In this relevant problem - diPRP - a picker tries to pick online orders while minimizing customer encounters. We model the problem as a Markov Decision Process (MDP) and solve it using a hybrid solution approach comprising mathematical programming and reinforcement learning components. Computational experiments on synthetic instances suggest that the algorithm converges to efficient policies. Furthermore, we apply our approach in the context of a large European retailer to assess the results of the proposed policies regarding the number of orders picked and customers encountered. Our work suggests that retailers should be able to scale the in-store picking of online orders without jeopardizing the experience of offline customers. The policies learned using the proposed solution approach reduced the number of customer encounters by more than 50% when compared to policies solely focused on picking orders. Thus, to pursue omnichannel strategies that adequately trade-off operational efficiency and customer experience, retailers cannot rely on actual simplistic picking strategies, such as choosing the shortest possible route.
翻译:零售业的演变带来了新的挑战,并提出了关于如何重新改造商店和供应链以满足在线渠道日益增长的需求的关键问题。全港零售商最近采取的措施之一是,通过商店内部挑选解决在线销售的增长问题,从而允许使用现有资产在线订单。然而,随着对离线客户经验的损害,零售业的演变带来了负面因素。为了根据零售商店活跃的客户流动情况来选择政策,我们正式确定了一个新的问题,即动态店内采摘者路由问题。在这个相关的问题 - DIPRP - 一位采摘者试图选择在线订单,同时尽量减少客户遇到的问题。我们将这一问题作为马可夫决策程序(MDP)的模式,并使用混合解决方案方法解决这一问题,包括数学方案编制和强化学习组成部分。合成实例的实验表明,算法与高效的政策相融合。此外,我们采用欧洲大型零售商的方法来评估拟议的关于选购订单和客户数量的政策的结果。我们的工作表明,零售商应当能够将内部选择在线定购单,而不是仅仅选择在线定购单的操作效率,而无需牺牲采购成本。我们的工作建议,零售商应当将市内选择客户政策的范围扩大到更精确地选择客户选择这样的采购方式,而不是选择更注重式采购。