Design and Implementation of Intelligent Logistics Equipment Scheduling Platform based on Internet of Things and Cloud Computing

At present, science and technology are widely used in modern logistics industry, and the realization of intelligent logistics scheduling and warehousing has also become a research hotspot. However, the logistics distribution environment is becoming more and more complex, and the logistics distribution tasks facing it are becoming more and more burdensome. This article designs an intelligent logistics equipment scheduling platform based on the cloud computing and Internet of Things (IoT), integrating information such as cargo source information, vehicle source information, and product introduction. In this paper, an improved Maximum-Minimum ant colony algorithm (MMAC) is designed to solve the logistics vehicle routing problem, an intelligent vehicle scheduling problem that introduces weight factors is proposed, and the effect of related parameter settings on the algorithm performance is analyzed. Overall, using the algorithm proposed in this paper to allocate order tasks, the total running distance of the system still shows a downward trend, which proves the effectiveness of the algorithm in the task allocation of intelligent logistics equipment scheduling platform. Based on warehousing logistics, this paper studies and models the scheduling problem in intelligent warehousing, divides the scheduling problem into two parts: task allocation and path planning, and conducts modeling and analysis respectively. The convergence speed of genetic algorithm is relatively fast, while the convergence speed of ant colony algorithm (AC) is relatively slow, but it can get more accurate results. After comparing these three algorithms, in terms of overall convergence speed, the optimized scheduling algorithm proposed in this article is slow, but relatively optimal results can be searched.