玩捉迷藏：解决店内拣选操作，同时改善客户体验
Playing hide and seek: tackling in-store picking operations while improving customer experience
摘要：
零售业务的发展带来了新的挑战，并提出了如何重塑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。全渠道零售商最近采取的措施之一是使用店内拣选来解决在线销售的增长，这允许使用现有资产来服务在线订单。然而，它带来了损害离线客户体验的缺点。为了实现拣选策略适应零售商店的动态客户流，我们正式提出了一个新的问题，称为动态店内拣选路由问题（diPRP）。在这个相关的问题- diPRP -一个采摘尝试挑选在线订单，同时尽量减少客户的遭遇。我们将问题建模为马尔可夫决策过程（MDP），并使用包括数学规划和强化学习组件的混合解决方案来解决它。合成实例的计算实验表明，该算法收敛到有效的政策。此外，我们应用我们的方法在一个大型的欧洲零售商的背景下，评估结果的建议政策的数量挑选和遇到的客户。我们的工作表明，零售商应该能够在不损害线下客户体验的情况下，扩大在线订单的店内拣选。与只关注拣选订单的策略相比，使用建议的解决方案方法学习的策略将客户接触的数量减少了50%以上。因此，为了追求充分权衡运营效率和客户体验的全渠道策略，零售商不能依赖于实际的简单化拣选策略，例如选择最短的可能路线。
英文摘要：
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.
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