Analytics for IoT‐Enabled Human–Robot Hybrid Sortation: An Online Optimization Approach

Motivated by a realworld practice of a China Post sortation center, this study considers the deployment of Internet of Things (IoT) technology to improve the efficiency of a human–robot hybrid sortation system. In this system, IoT technology enables responsive adjustment of the manual capacity to alleviate the congestion effect of increasing parcel flow on robotic sorting efficiency. We begin with a predictive analysis of a real‐life dataset to quantify the congestion effect on robotic sorting efficiency, and examine the nonstationary behavior of the parcel flow process. Subsequently, we design an online algorithm using IoT advance information (which refers to advance observation of some parcel flows before their actual arrivals) for efficiently adjusting the manual capacity. We develop theoretical guarantee for the effectiveness of the online algorithm by bounding its regret, and also demonstrate that the long‐term expected regret rate is zero under mild conditions. Via extensive simulation experiments, we find that our online algorithm outperforms the China Post sortation center's current policy and conventional Markov dynamic program in terms of computational efficiency and solution quality. The simulation results also demonstrate that the value of IoT technology to the sortation center can be significant, and shed insights on IoT investment by revealing the diminishing return of expanding the horizon of IoT advance information.