Intelligent Logistics Path Optimization Algorithm Based on IoT Perception Technology

Logistics path optimization has been aided by intelligent computing and Internet of Things (IoT) technologies in recent years. Path planning and optimization considers diverse hindrances and augmenting features associated with vehicles, routes, delivery, etc. through intensive assessments. In this article, a divergent transform learning optimization algorithm is introduced to improve the path-planning abilities of intelligent logistics services. This process is interconnected through the IoT paradigm for sharing real-world information such as coverage distance, delivery, and freight expenses to decide an optimal target factor. Based on the differences between each factor, the divergent condition for optimization transform is applied. Thus, the transform is either single or multi-factor induced to improve the delivery rate with less target error. The divergent learning replicates the transform process repeatedly until maximum factors are optimized to recommend a reliable route with fewer decision times. Besides, the learning algorithm is trained using the varying target factors over the successful path discovered and logistics delivered.