Research On Warehousing Logistics Optimization Under Fog Computing Environment

Emerging technologies such as the Internet of Things and cloud computing have brought great changes to the traditional production mode of logistics,and the emerging technologies have been widely used in intelligent warehouses.As an important part of intelligent logistics,intelligent warehouse has deployed a large number of Io T devices in the intelligent warehouse.The analysis and processing of tasks bring a lot of challenging data to cloud computing.The cloud computing system applied in the intelligent warehouse is managed in a centralized way and cannot make full use of the resources of the intelligent warehouse.Moreover,the existing factors such as remote deployment and shortage of network resources lead to the unsatisfactory execution effect of tasks.It is unrealistic and costly to require the cloud computing layer to process every request uploaded from the warehouse.The traditional remote centralized task processing method cannot meet the requirements of various goods flow tasks inside the intelligent warehouse with higher delay requirements.Therefore,a distributed fog computing method is adopted,and fog computing nodes are deployed near intelligent terminal devices to shorten the transmission distance between tasks to be processed and computing resources,so as to provide real-time and rapid response.The fog node is closely deployed with all kinds of physical machines and terminal equipment in the intelligent warehouse,and provides real-time computing services through network connection.The advantage of fog computing is that it can analyze a large amount of data generated by intelligent warehouse in real time,analyze and process complex tasks,and quickly and efficiently complete tasks delivered by terminal equipment.However,only reasonable allocation and scheduling of fog computing resources can meet the requirements of intelligent warehouse for time delay and energy consumption.Firstly,according to the requirements of the intelligent warehouse and the characteristics of the basic architecture of fog computing,this paper designs the architecture of the intelligent warehouse based on fog computing,and provides two different computing methods for the relevant tasks in the intelligent warehouse,and establishes the mathematical model under the terminal computing mode and the fog layer computing mode.Then,the artificial bee colony algorithm was improved by introducing the idea of reverse learning and improving the search equation to enhance the algorithm’s performance to meet the requirements of fog node computing resource scheduling under the fog layer computing mode.Then,the task scheduling model in the intelligent warehouse is established,and the time delay and energy consumption analysis model is established.The improved artificial bee colony algorithm is applied to the computing resource allocation of fog nodes in the intelligent warehouse.In the fog layer computing mode,the fog nodes are allocated the optimal data amount and reasonable network bandwidth to achieve the reasonable distribution of task information flow,so as to meet the requirements of intelligent warehouse tasks for time delay.Finally,based on the analysis and processing ability of the fog computing layer,aiming at the problem of the unmanned moving vehicle handling goods in the intelligent warehouse,the path optimization of the moving vehicle in the intelligent warehouse is carried out through the genetic algorithm,so as to minimize the distance of the moving path.Simulation experiments and results show that,in the fog computing environment,the improved algorithm can allocate computing tasks to the fog nodes in the fog layer computing mode,which can allocate the best amount of computing data and network bandwidth for the fog nodes.The task information flow scheduling between the intelligent terminal device and the fog node is realized,so that the absolute error between the task completion delay and the task calibration delay on the fog node is within the error range,which can meet the time delay requirements of different task types.Through genetic algorithm,the transportation path of goods at different positions in the intelligent warehouse can be planned,and the shortest transportation path of the truck can be realized.The analysis results show that the logistics task optimization of the intelligent warehouse is efficient and feasible under the fog computing environment.