Research On Production Scheduling Based On Improved Geneticsimulated Annealing Algorithm

Production scheduling is an integral part of the production process,which plays an important role in the overall operation of manufacturing enterprises.With the development and wide application of network technology and communication technology,the worldwide communication is more and more frequent,and the trend of economic globalization is becoming more and more obvious.At the same time,the improvement of social material conditions leads to the change of market demand,and the market is becoming more and more diversified and customized.The competition of the globalization and the change of the market lead to the traditional production mode of the enterprise,which is difficult to meet the requirement of long-term survival.Small varieties,customized production methods are gradually being adopted by more and more enterprises,such a mode of production to the workshop scheduling put forward higher requirements.How to use modern technology and method to achieve overall optimization of production scheduling,to achieve fast and accurate scheduling,rapid deployment of resources,speed up the production efficiency of enterprises has become a large manufacturing enterprises need to solve the problem.In this paper,the mathematical model of production scheduling problem and the application of simulated annealing algorithm and genetic algorithm in the job shop scheduling problem are studied.According to the characteristics and advantages and disadvantages of the two algorithms,a genetic simulated annealing algorithm is proposed.Finally,the improved genetic simulated annealing algorithm is applied to solve the practical problem,and the feasibility and practicability of the algorithm is further verified.This paper conducted in-depth research and application of optimized algorithm for genetic simulated annealing algorithm in production scheduling problem,through mutation operator and selection operator algorithm for improving the optimization performance of the algorithm and the ability to avoid the "premature" problem.