The Online Order Batching Problem (OOBP) is a variant of the well-known Order Batching Problem (OBP). As in the OBP, the goal of this problem is to collect all the orders that arrive at a warehouse, following an order batching picking policy, while minimizing a particular objective function. Therefore, orders are grouped in batches, of a maximum predefined capacity, before being collected. Each batch is assigned to a single picker, who collects all the orders within the batch in a single route. Unlike the OBP, this variant presents the peculiarity that the orders considered in each instance are not fully available in the warehouse at the beginning of the day, but they can arrive at the system once the picking process has already begun. Then, batches have to be dynamically updated and, as a consequence, routes must too. In this paper, the maximum turnover time (maximum time that an order remains in the warehouse) and the maximum completion time (total collecting time of all orders received in the warehouse) are minimized. To that aim, we propose an algorithm based in the combination of a Greedy Randomized Adaptive Search Procedure and a Variable Neighborhood Descent. The best variant of our method has been tested over a large set of instances and it has been favorably compared with the best previous approach in the state of the art.

在线订单批处理问题（OOBP）是众所周知的订单批处理问题（OBP）的变体。与OBP一样，此问题的目标是遵循订单批处理拣货策略来收集到达仓库的所有订单，同时最小化特定的目标功能。因此，在收集订单之前，将具有最大预定义容量的订单进行分组。每个批次都分配给一个拣配器，该拣配器通过一条路线收集批次内的所有订单。与OBP不同，此变体的独特之处在于，每种情况下考虑的订单在一天的开始时在仓库中都无法完全使用，但是一旦拣配过程已经开始，它们就可以到达系统。然后，必须动态更新批次，因此，路线也必须更新。在本文中，最大周转时间（订单保留在仓库中的最长时间）和最大完成时间（仓库中收到的所有订单的总收集时间）最小。为此，我们提出了一种基于贪婪随机自适应搜索过程和可变邻域下降相结合的算法。我们的方法的最佳变体已在大量实例中进行了测试，并且已与现有技术中的最佳先前方法进行了比较。