A Bi-GRU and SSA integrated framework for green logistics optimization in IoT-enabled smart cities

This study proposes a green logistics optimization model leveraging IoT data for route planning and energy efficiency in smart cities. The primary objective of this model is to address the traffic scheduling challenges in automated logistics transportation, enhance transportation efficiency, and minimize energy consumption. A novel model integrating the Sparrow Search Algorithm (SSA) and Bidirectional Gated Recurrent Unit (Bi-GRU) is proposed. SSA is first employed to optimize route planning, taking into account environmental factors such as traffic congestion, thereby providing a globally optimized initial solution. Subsequently, Bi-GRU adjusts the route in real-time according to historical data, including vehicle speed and cargo status. This integration fully exploits the complementary advantages of the two algorithms: SSA’s global optimization ability and Bi-GRU’s dynamic adjustment based on time-series information. Experimental results demonstrate that the application of this method can significantly reduce the unit transportation time of goods by 38.75% and the unit transportation energy consumption of goods by 23%. Finally, the paper explores the prospects of green logistics development based on Internet of Things technology in the development of smart cities, offering insights for future research and practical applications.