Order batching plays a pivotal role in enhancing order fulfillment efficiency within both manual and robotic warehousing systems. Rare attention has been devoted to the impact of future incoming orders on online order batching. This study addresses this gap by exploring the potential benefits of reserving suitable orders when upcoming orders share similarities with existing orders in the order pool. Specifically, we investigate the online order batching problem with predictive order reservation, employing the Ensemble Learning method, to predict similarities between current and future orders. Our proposed approach involves deliberate reservation of certain orders upon arrival, deferring their batching to a subsequent period for additional efficiency gains. To operationalize this predictive order reservation, we develop an algorithmic framework that comprehensively addresses online order batching, encompassing batching, sequencing, and assignment. Experimental results, conducted on real data from an e-commerce warehouse, demonstrate the superiority of our proposed approach over fixed and variable time-window online batching algorithms in terms of order turnover time, with improvements of up to 6.1%. Notably, the benefits are more pronounced when the order arrival rate aligns with the available picking resources. Note to Practitioners—This paper was motivated by the practical problem of predictively reserving orders to improve the holistic efficiency of online batch picking in e-commerce warehouses. Existing approaches generally have assumed that warehouse management system (WMS) releases all orders in the order pool to generate the order batch and not considered the potential benefits of order reservation. This paper proposes a new online order batching approach with order reservation using machine learning. The proposed approach could bring many benefits to practitioners. Firstly, the proposed algorithm could improve the quality of order batch and could easily be embedded in the existing WMS without interference to current operation process. Secondly, the proposed algorithm framework could be easily re-configured to cover diverse scenarios of operation strategy combination and used to evaluate the performance of different methods. Thirdly, the proposed online order batching approach with order reservation using machine learning can be used to various order picking systems including manual order picking system, human-robot collaborative order picking system and robotic order picking system. In future research, we will address the order reservation mechanism considering different objective functions, such as minimizing tardy orders or variance of order turnover time and explore its applicable scenarios.

中文翻译：

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使用机器学习通过预测性订单预订提高电子商务仓库订单履行率


订单批量在提高手动和机器人仓储系统中的订单履行效率方面发挥着关键作用。人们很少关注未来收到的订单对在线订单批处理的影响。本研究通过探索当即将到来的订单与订单池中的现有订单具有相似性时保留合适订单的潜在好处来解决这一差距。具体来说，我们研究了具有预测订单保留的在线订单批处理问题，采用集成学习方法来预测当前订单和未来订单之间的相似性。我们提出的方法包括在到达时故意保留某些订单，将其批处理推迟到随后的时期，以提高效率。为了实施这种预测性订单预订，我们开发了一个算法框架，全面解决在线订单批处理问题，包括批处理、排序和分配。对电子商务仓库的真实数据进行的实验结果表明，我们提出的方法在订单周转时间方面优于固定和可变时间窗口在线批处理算法，提高了高达 6.1%。值得注意的是，当订单到达率与可用的拣选资源一致时，好处会更加明显。从业者须知——本文的出发点是预测性保留订单的实际问题，以提高电子商务仓库在线批量拣选的整体效率。现有方法通常假设仓库管理系统（WMS）释放订单池中的所有订单来生成订单批次，而没有考虑订单预留的潜在好处。 本文提出了一种使用机器学习进行订单预订的新在线订单批处理方法。所提出的方法可以给从业者带来很多好处。首先，所提出的算法可以提高订单批次的质量，并且可以很容易地嵌入到现有的WMS中，而不干扰当前的操作流程。其次，所提出的算法框架可以很容易地重新配置，以覆盖不同的操作策略组合场景，并用于评估不同方法的性能。第三，所提出的使用机器学习进行订单预订的在线订单批处理方法可用于各种订单拣选系统，包括手动订单拣选系统、人机协作订单拣选系统和机器人订单拣选系统。在未来的研究中，我们将考虑考虑不同目标函数的订单保留机制，例如最小化延迟订单或订单周转时间的方差，并探索其适用场景。