Intelligent Supply Chain and Logistics Route Optimization Algorithm in Wireless Sensor Network.

This article first proposes the design of the ZigBee wireless sensor network node based on Arduino, from the design and implementation of the node hardware system to the design and implementation of the node software system, and expands on the design details. The node function was applied to the node design environment, and simulation tests were performed, and execution was tested to confirm the feasibility and practicability of the design. Based on this, the article proposes the service construction process of the software and hardware platform of the smart supply chain system, and then clarifies the basic mathematical problem of the distribution plan in line with the service strategy, that is, "the logistics route optimization algorithm with capacity and time constraint optimization," which aims to study statically design, define, and model, and test two precise solutions representing heuristic genetic algorithms and analyze their results. In addition, additional hardware such as smart supply chain and cabinet boards are tested and designed according to system requirements, and the interaction mechanism is adapted to the user interface and supporting structure design so as to ensure the completion of user-related business processes. Combining the existing theoretical knowledge, the first priority is to meet customer needs, build a smart supply chain model under the premise of on-time delivery, and solve the model in time. Based on this point, this article tries to use hybrid branch algorithm link and tabu search algorithm to increase the consideration of traffic conditions, collect accurate algorithms, and modern heuristic algorithms to solve the actual situation of the case. At the end, the article proposes some related issues for the above problems. Suggesting through the research of wireless sensor network, this paper applies it to the research of intelligent supply chain and logistics route optimization algorithm and promotes the development of more intelligent supply chain in the future.