Research On Intelligent Scheduling Of Logistics Resources In Cloud Manufacturing Environment

The booming development of the new generation of information technology is driving a new industrial revolution to reshape the current manufacturing industry and provide opportunities for my country’s manufacturing transformation and upgrading to achieve curve overtaking.As a new model,cloud manufacturing is a service-oriented network-oriented manufacturing model.With the widespread transportation of cloud manufacturing to various production areas,now,cloud manufacturing has already become an important means for my country’s manufacturing enterprises to achieve high-end fields,transformation and upgrading,and achieve high-quality development.Among them,the logistics industry is one of the general use of scenarios in the cloud manufacturing model.As a national strategic industry,the development level of the logistics industry reflects the level of economic development and industrial prosperity of a country.The technical level of the optimized and scheduling of logistics resources determines whether it can ensure high-quality and high-efficiency distribution.Logistics resource demand forecasting can provide logistics enterprises with logistics support,optimize warehouse layout in advance,and reduce the impact of the bullwhip effect on customers.Logistics resource scheduling needs to fully consider the real-time status information of customer demand collection and schedulable resources on the platform,develop scheduling plans,and make good path planning.In this process,a more accurate prediction of logistics resources is needed to facilitate subsequent scheduling.The most cumbersome changes in customer demand during the scheduling process require consideration of complex and ever-changing delivery situations,which is one of the difficulties that cloud manufacturing mode must solve when applied in the logistics industry.Therefore,with the support of the Jilin Provincial Department of Education project "Research on Resource Dynamic Scheduling Method in Cloud Manufacturing Environment",this article conducts research on the intelligent scheduling problem of logistics resources in cloud manufacturing environment.The research content of this article is as follows:1)Introduced the research background,and after thoroughly reading and analyzing a large number of relevant domestic and foreign literature,gained an understanding of the latest research results and progress.On this basis,determine the research purpose and significance,clarify the research direction of intelligent scheduling of logistics resources in cloud manufacturing environment,and conduct in-depth research on logistics resource demand prediction,optimal scheduling scheme,and distribution path scheme in cloud manufacturing environment.2)Clarify the issues to be addressed in logistics resource demand prediction.In a cloud manufacturing environment,evaluate the service capacity required for upcoming tasks based on the customer’s published culture on the platform,and arrange appropriate service windows for the tasks to arrive in advance.A logistics resource demand prediction model based on CNN-LSTM neural network algorithm is proposed to accurately predict the demand of customers.The simulation experiments have shown that the model can accurately predict customer demand under multiple factors,providing a scientific basis for the next reasonable adjustment of scheduling plans.3)In response to the uncertainty in logistics resource scheduling,logistics resource scheduling in the cloud manufacturing environment is achieved through cloud platforms to bring benefits to future resource providers with idle resources and meet the urgent needs of logistics resources.This article adopts an improved hybrid parameter ant colony algorithm and considers multiple factors to develop a vehicle scheduling plan based on time series.In order to transport logistics resources,considering the vehicle load capacity and interior volume,loading planning should be carried out to rationalize the loading of logistics resources in the vehicle scheduling plan.The Clonal selection algorithm integrating Reverse learning of cloud model is selected,and the optimal path optimization scheme is formulated and the optimal distribution path is solved.4)The purpose of this article is to achieve the interconnection of all things and verify the operability of the system.We designed a cloud manufacturing logistics resource scheduling platform,embedded the logistics resource prediction model and logistics resource scheduling model in the article,and verified the performance of the model by operating on the cloud platform.