Research And Development Of Advanced Scheduling Key Technology For Discrete Manufacturing Based On Industrial Internet

With the rapid development of national economy,the material needs of people’s lives have also increased,and the demands for products have become increasingly personalized,leading to smaller batch and customized products produced by enterprises.Subsequently,production scheduling has become increasingly complex,resulting in low production efficiency and slow delivery times due to a lack of experienced professional scheduling personnel and advanced scheduling software systems.Advanced Planning and Scheduling(APS)is the rapid development of reasonable and specific production scheduling plans with limited capacity and variable production lead times under numerous constraints.Job shop scheduling is a fundamental component and important goal of advanced planning and scheduling,which can effectively improve production efficiency in the context of multi-order small batch customization production.However,due to the independence of various production links in traditional production enterprises,the APS system is disconnected from actual production,making it difficult to track and adjust the real-time operation status of the workshop,resulting in low production efficiency and economic benefits for the enterprises.Aiming at the above problems,this paper studies the flexible job shop scheduling problem based on the Industrial Internet and the improved tuna swarm optimization algorithm to optimize the order completion time.The Industrial Internet is used to collect the order changes and equipment status in real time,and an improved tuna swarm algorithm is used to dynamically optimize the scheduling of flexible workshop scheduling,so as to achieve the goal of shortening order completion time,improving equipment utilization and optimizing scheduling.The main research contents of this article are as follows:(1)The flexible job shop scheduling problem is modeled mathematically,the key parts of the problem are represented by mathematical notation,the constraints in the problem are analyzed and expressed by mathematical formulas,and the objective function is established with the minimum and maximum completion time as solution objective,so that it can be solved by heuristic swarm intelligence algorithm.(2)Research and improve the heuristic intelligent algorithm for tuna schools,propose adaptive updates to improve the optimization ability and convergence accuracy of the algorithm,and make it the core algorithm for flexible workshop scheduling optimization solution.To ensure the diversity of the initial population,chaos mapping is adopted to initialize the population;Design adaptive operators for the two random update strategies of the tuna swarm algorithm,enabling the algorithm to select appropriate update strategies in different situations,and combine the update method with the Levy flight strategy to enhance the search range of the algorithm in search space;In the later iteration stage of algorithm solving,in order to prevent the algorithm from falling into local optima,a mutation strategy is designed for the algorithm,introducing concepts such as rejection concentration and degree of superiority,and selecting partial solutions for mutation.(3)Applying the improved tuna swarm algorithm to the production planning and scheduling of flexible job shops to implement the APS system,ROV rules are adopted to discretization the improved algorithm.For the job shop scheduling problem,single coding is adopted to code it,and the machining machine is selected for the process based on the machine gap.Comparing the applied algorithm with other heuristic improvement algorithms,the superiority of the adaptive improved tuna swarm algorithm based on machine gaps in solving workshop scheduling is demonstrated.(4)Develop APS scheduling function module based on Industrial Internet in enterprise production intelligent management platform,build My SQL database,develop user interface based on Vue and Element UI,obtain relevant data from OA system,ERP system and workshop collection system through RESTful mode,and use adaptive improved tuna swarm algorithm for production scheduling.