Affiliations: School of Digital Commerce, Jiangsu Vocational Institute of Commerce, Nanjing, Jiangsu, China | E-mail: 17314958873@163.com
Correspondence:
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Corresponding author: School of Digital Commerce, Jiangsu Vocational Institute of Commerce, Nanjing, Jiangsu, China. E-mail: 17314958873@163.com.
Abstract: Accompanied by a series of developments in information technology, such as the Internet of Things, big data, and digital twin technology, these innovations came into existence and began to gain significance. Targeting the issues of hierarchical confusion and inadequate visualization in traditional logistics and warehousing systems, this study begins by analyzing the framework structure of the warehousing system. It uses genetic algorithm calculation to obtain the solution set for optimizing cargo pull objectives. Finally, it proposes a novel intelligent IoT logistics and warehousing system by integrating digital twin technology. The experiment results indicated the genetic algorithm could optimize up to 60% of the cargo pull optimization objective function in this model with at least 300 iterations. The simulation and actual times of outgoing and incoming storage under this model varied between 0 to 1. The error throughout the range was a minimum of 0.1 seconds. The study found that the storage density achieved a maximum value of nearly 98%, while the minimum storage cost was approximately $3 per order and the maximum was $9 per order. Overall, the proposed model can aid enterprises in optimizing their operations by improving efficiency and reducing logistics and warehousing costs, ultimately promoting the digital and intelligent development of the logistics industry.
Keywords: Internet of things, logistics and warehousing systems, digital twins, genetic algorithms, cargo pull optimization
