Research On Intelligent Scheduling Algorithm And Optimization Model Of Digital Workshop

Since the reform and opening up,in order to meet the needs of domestic infrastructure construction and the improvement of people's living standards,coupled with the deepening of global economic integration and the increasing international competition,the pace of product renewal and iteration is getting faster and faster,and China's manufacturing industry is continuing.rapid development.In recent years,with the introduction of “Industry 4.0” and “Made in China 2025”,the diversified,multi-variety and small-batch production methods have been valued by many companies.This discrete production method must improve the ability of the company's shop scheduling to improve the production efficiency of the enterprise.This paper analyzes the problem of shop scheduling optimization from two directions.Firstly,it considers the problem of shop scheduling load balancing,describes and establishes the mathematical model.Secondly,the new whale optimization algorithm improved hybrid genetic algorithm to solve the problem of shop alignment.First,the shop scheduling is flexible in actual production.Each workpiece contains one or more processes,and each process can be processed on multiple machines.Considering that selecting a machine for workpiece machining requires a reasonable assignment of processing tasks,balancing equipment loads and improving overall operational efficiency.This paper proposes and constructs a mathematical model of flexible job shop scheduling load balancing.By calculating the load mean square error of each machine to reflect the current load of the machine,the smaller the load mean square error,the smaller the load of the machine,the more priority should be given to the processing tasks.Secondly,to improve the performance of WOA,an adaptive dual-population whale optimization algorithm(DPWOA)is proposed.DPWOA starts with two randomly initialized populations and then evolves their own population without interfering with each other.The main population is designed to solve the optimal solution of the problem,and the reserve population aims to provide diversity for the main population.The effectiveness of the improved algorithm is verified by experiments,and the convergence speed and convergence precision of the WOA algorithm are enhanced.Thirdly,this paper proposes a hybrid DPWOA algorithm and a genetic algorithm to solve the shop scheduling optimization algorithm DPWOAGA.In order to make up for the problem that WOA can't solve the permutation and combination,the vector position coding of the whale individual can be converted into the coding form of the chromosome that can be scheduled.In the three sets of examples in the shop scheduling,experiments have shown that the DPWOAGA algorithm can find smaller maximum completion time and the most balanced machine load.It is further verified that the proposed algorithm is an efficient and feasible hybrid intelligent algorithm.