The Research On Job Shop Scheduling Problem Based On Genetic Algorithm

As the time of global economy integration and knowledge economy is coming, the competition between enterprises will be more drastic. In order to increase their core capability of competition, enterprises must improve their inner production management, especially its core technique-production scheduling. Nevertheless, scholars often focused on certain job-shop scheduling problem in the past, but there are many uncertain factors in reality, and the process time and due date are usually uncertain. This thesis researches on fuzzy job-shop scheduling problem in depth based on existing theories.Through the review of the changes of manufacture circumstance and researches on the methods about production scheduling, this thesis provides the theoretical model on the problem of job-shop scheduling. Based on the complexity of computing and mathematics model, several important adjacent region searching algorithms which are the most effective intelligent methods until now are discussed in job-shop scheduling problems. Because of the local astringency of genetic algorithm, an improved recurrence search algorithm is presented based on the essential genetic algorithm. This algorithm is comprised of the keep optimization method and adaptive adjustment method and so on. An improved genetic algorithm for client satisfaction is presented with fuzzy process time and due-date time in order to solve the uncertainty factors in job-shop scheduling problems. To satisfy the requirement of multi-variety and multi-object of enterprise, this thesis researches on the infection of objective function to job-shop scheduling problems and the relation of the objective functions. The multi-objective fuzzy genetic algorithm uses weight analysis to get the optimization solution.The improved genetic algorithm is validated by the benchmark problems of fuzzy job-shop scheduling. The improved fuzzy genetic algorithm which is based on agreement index based on agreement index shows the relation between fuzzy process time and fuzzy due date time and it can get the solution with the maximize client' satisfaction in job-shop scheduling problems. The multi-objective genetic algorithm is based on the fuzzy genetic algorithm and solves the impact between diversified objective function, it can get the solution according to the requirement of practical product.The theoretic analysis and the experience result prove that the improved genetic algorithm in this thesis can solve the local minimum of genetic algorithm. And the multi-objective function shows the clients' practical requirements about job-shop scheduling...