Research And Application Of Shop Scheduling Optimization Based On Improved Artificial Bee Colony Algorithm

In the context of social progress,the rapid development of science and technology,manufacturing enterprises are facing challenges,and competition among enterprises is constantly upgrading.In an increasingly competitive market,in order to provide customers with more and better products,companies must change their workshop production scheduling to adapt to market changes.The fundamental way to change the production scheduling method of the workshop is to improve the production scheduling technology of the workshop,so that the production scheduling of the workshop not only depends on the wisdom and experience of the people,but also depends on the scientific method.At present,the researcher of production scheduling direction has become more and more,and many solving methods have been designed on the shop scheduling problem.The artificial bee colony algorithm with few parameters,simple design and easy implementation is highly efficient because of its high efficiency.Widely used in the solution of various shop scheduling problems.This paper studies the problem of shop scheduling and designs an efficient solution method with the minimum completion time as the goal.Firstly,the classification methods and actual existence characteristics of the shop scheduling problem are analyzed.The research trends of domestic and foreign scholars and their various methods used in solving the shop scheduling problem are summarized.Then,the artificial bee colony algorithm with less parameters and strong global search ability is used to solve the shop scheduling problem,which improves the efficiency of the workshop scheduling and saves the time spent in the production of the job shop.For the artificial bee colony algorithm,the design principle of the algorithm,the characteristics of the algorithm and the advantages and disadvantages of the algorithm in solving the problem are mainly studied.The improvement makes up for the shortcomings of the algorithm to solve the discrete problem,that is,introduces and improves the mutation mode and crossover mode of the genetic algorithm to strengthen the search ability of the algorithm.Then the improved mutation method and crossover method are used as the way to lead the bee and follow the bee search solution in the artificial bee colony algorithm,which changes the search strategy of the algorithm and the domain structure of the solution.In this paper,the performance of the algorithm is verified by simulation.The classic job shop scheduling problem is used to test and prove the performance of the algorithm.Finally,this paper takes the metal manufacturing stamping workshop as an example,and uses the algorithm designed in this paper to develop an information management system for its production scheduling.The analysis summarizes the functional requirements of the shop scheduling management system and designs the architecture and modules of the system.According to the analysis of the requirements of the system,this paper designs and optimizes the database structure.Using the B/S system architecture,a simple and friendly front-end interface was designed,and the development work was completed using the Java language.In this paper,the improved algorithm is applied to the production scheduling module of the shop scheduling management system,which makes the workshop scheduling more intelligent and efficient,and also improves the informationization level of enterprise workshop scheduling.