Application Of Improved Ant Colony Algorithm In Manufacturing Shop Scheduling

By the quickly progress of science and technology,the manufacturing industry has begun to rely on computing technology to intelligent manufacturing.Intelligent optimization algorithm can help enterprises to quickly find a scheduling scheme that meets the production objectives,solve the problem of low production efficiency,and improve the competitiveness of enterprises.The previous basic algorithms are difficult to find the global optimal solution and the calculation time is too long when dealing with the complex scheduling problem of manufacturing workshop.To find more effective solutions,scholars have begun to try to improve the shortcomings of basic algorithms in recent years.Based on flexible job shop problem,this text designs an improved ant colony algorithm.By improving the state transition rules,pheromone update mode and other key steps in the algorithm,it improves the fast optimization ability of the algorithm.The main contributes of this txet are as follows:Firstly,genetic algorithm is used to deal with the initial population,and some excellent individuals are selected to complete the crossover and mutation operation.The pheromone obtained is used as the initial pheromone of ant colony algorithm after secondary processing.In the initial stage,the priority of the machine is sorted according to the machine attributes,which saves the search time.The adaptive random proportion rule is used to modify the prior parameter value and the state transition rule of ant colony algorithm to meet the requirements of the algorithm in different stages;A new heuristic function is designed,in which a correction function is added to solve the blind search of ants when the difference of candidate nodes is not obvious,and enhance the guiding role of pheromone.New global update rules are designed to expand the gap between advantage path and disadvantage path by modifying pheromone update strategy;The maximum minimum ant system is used to deal with the concentration of pheromone to enhance the ability of fast convergence to the optimal solution in the later stage of the algorithm and reduce the optimization time.In this paper,the specific process of solving the flexible job shop scheduling problem is given,and the algorithm mathematical model is constructed.The algorithm designed in this paper is run in MATLAB,and two sets of classic examples are used to simulate the results.The results are compared with the data of other relevant documents,and the algorithm designed in this paper has improved the precision and convergence rate.The algorithm is applied to the job shop scheduling management system to solve the actual job shop scheduling problem.The results show that the algorithm designed in this paper has good stability and practicability.