Research On Production Scheduling Optimization Of Intelligent Factory

Intelligent factory has the characteristics of intelligent production system,digitization and dynamic decision making.As an important part of intelligent factory,production scheduling should be data-driven and can realize real-time dynamic decision.Therefore,this paper focuses on issues related with production scheduling in smart factories,and the main research contents are as follows:(1)Analysis of production scheduling problems in intelligent factories.Firstly,the architecture and key technologies of intelligent factory are analyzed,which point out that the difference between intelligent factory and traditional factory production scheduling is the data drive is the core or not.On this basis,the production scheduling characteristics and key technologies of flexible job shop and distributed flexible job shop are analyzed under the background of intelligent factory.(2)Research on production scheduling of flexible job shop in intelligent factory.In the Internet of things environment,real-time manufacturing data is collected based on wireless sensors such as RFID,and the real-time manufacturing data is transmitted to the corresponding Agent model in CPS.Thus,we formed a solution based on CPS agent.In the Agent model,biota intelligent communication is used to replace the traditional contract network auction model,the pheromone and transfer coefficient based on the random disturbance ant colony algorithm are designed,and the calculation strategy of pheromone and transfer coefficient based on real-time data is given.Finally,the validity of the proposed model is verified by simulation.(3)Research on scheduling of flexible job shop in intelligent factory.For the enterprises with multiple workshops with the same processing capacity,the multi-workshop production system is divided into three sub-systems of planning-scheduling-logistics based on ATC,and the hierarchical model of production scheduling is established.The production disturbance is divided into grades,and the first-order rescheduling or second-order rescheduling is determined according to the influence degree of the disturbance events.Finally,the tabu genetic algorithm is used to solve the problem.Simulation results show that the ATC multiobjective dynamic production scheduling model proposed in this paper can reasonably and effectively carry out scheduling,and is more efficient and accurate than genetic algorithm in terms of algorithm.(4)Intelligent factory production scheduling example verification.Based on the actual production data of company A's intelligent factory,the two models proposed in this paper are simulated and verified.The experimental results show that the scheduling models established in this paper for the two types of production scheduling problems in intelligent factories are effective and superior,which will bring theoretical support for the subsequent implementation of intelligent factories.