Exploring logistics transport route optimization: An algorithmic study based on RFID technology.

This article focuses on a logistics vehicle scheduling model that integrates Radio Frequency Identification (RFID) technology with an adaptive taboo search algorithm and nearest neighbor algorithm to optimize routing under random customer demand and service time conditions. The proposed VRPSDSST-STW model (Vehicle Routing Problem with Stochastic Demand and Service Time under Soft Time Windows) aims to improve scheduling efficiency, reduce queueing time, and lower transportation costs. Comparative experiments and a case study involving a logistics company demonstrated that the model outperforms traditional algorithms, achieving over a 25% reduction in total transportation cost and enhanced resource utilization. The study highlights the potential of combining RFID data with intelligent optimization algorithms to address dynamic logistics challenges, while noting limitations related to real-time disruptions such as traffic and equipment failures, suggesting future research on dynamic path adjustment methods.