Research On Job Shop Scheduling Based On Improved Genetic Algorithm

The job shop scheduling problem is an important part of manufacturing execution system, directly affects the economic benefits of the enterprise. The competition of hot market environment is getting more and more excited, how to use computer technology to achieve production scheduling optimization of various resources, production workshop, quick response to emergency situations, has become a major issue facing to many manufacturing enterprises.The production scheduling problem have various types and various solutions, The job shop scheduling is the most basic and most famous of machine scheduling problem, also proved to be the most complicated combinatorial optimization problems NP-hard. In order to solve this problem, experts and scholars has been paid effort of half of century, but so far the most advanced algorithm is still very difficult to get the optimal solution of the problem of small scale.This paper established the mathematical model of job shop scheduling problem, in the study of the traditional genetic algorithm, an improved genetic algorithm is proposed. A crossover operator based on POC encoding process with this algorithm, the offspring can inherit the excellent characteristics of the father generation; in order to overcome the existing problems of premature convergence in traditional genetic algorithm, the mutation operation design of a close relative relationship. Finally, relevant examples of genetic algorithm improved the test, the test results show that this algorithm can solve the scheduling problem more effectively than other genetic algorithm.