Logistics Capacity Optimization for Semi‐Finished Tobacco Warehouses Under Industrial <scp>IoT</scp> : A Simulation‐Based Approach

ABSTRACT Semi‐finished tobacco warehouses are critical nodes in cigarette manufacturing, where logistics capacity directly influences production efficiency. Under the framework of the Industrial Internet of Things (IIoT), this study develops a simulation‐based optimization approach to enhance warehouse logistics capacity. An EMS (Electrical Monorail System) trolley scheduling strategy based on a “farthest‐first” rule is proposed to improve track utilization and reduce congestion. One‐way experiments and ANOVA confirm that both the number of lanes and trolleys significantly affect system performance. A full factorial design combined with multiple regression analysis produces a well‐fitted (99.9%) agent model. Using the feasible direction method, the optimal configuration is identified as 11 lanes and 11 trolleys, with less than 2.5% deviation between fitted and simulated results. The proposed framework provides a quantitative and systematic method for logistics capacity design in IIoT‐enabled smart warehouses, offering both theoretical insights and practical guidance for intelligent logistics applications.