A Dynamic Operational Optimization Method for Robotic Mobile Fulfillment Systems with Inventory Discrepancy Events

A Robotic Mobile Fulfillment System (RMFS) is an emerging “cargos-to-person” picking system that relies on the broom of Internet of Things (IoT) technology. It aims to offer significant enhancements in order picking efficiency. However, dynamic disturbances, such as inventory discrepancies arising from errors in receiving, shipping, and handling of goods, often disrupt its operations, leading to degraded service and increased operational costs, thereby affecting overall system performance. Traditional optimization solutions may necessitate adaptations or overhauls in response to such disturbances. This paper introduces a proactive multi-pathway response algorithm tailored to mitigating dynamic disturbances in RMFS, particularly concerning inventory discrepancies. We extend an open-source simulation framework to evaluate the performance of the proposed algorithm and conduct a comparative analysis of dynamic systems. Experimental results indicate that our proposed algorithm can effectively improve the processing efficiency of abnormal orders with the minimal system-wide impact, highlighting its potential to well address dynamic and abnormal events in smart warehouses.