Adaptive order batching in warehousing based on real-time information



Nowadays, the current goal in warehouse operations is to design systems that make decisions on their own and to perform their tasks as autonomously as possible when reacting on changing exogenous conditions. This trend puts a greater emphasis on the most critical operation in a warehouse, order picking. In this talk we introduce a new order batching method used in the order picking process called, adaptive order batching. In adaptive order batching incoming customer orders are dynamically batched and a pick batch can be modified in real-time even when the pick tour has already started. Adaptive order batching is advantageous as it allows for the inclusion of an urgent order in the pick batch, last minute changes to an order in the pick batch, and an increase order picking responsiveness. We develop a mathematical framework for adaptive batching that minimizes the order throughput time of incoming customer orders. Adaptive order batching is shown to outperform other conventional batching methods under various performance conditions.