Shop Scheduling Based On Genetic Algorithm And Rough Set Theory

This dissertation studies the improvement problem of the genetic algorithm, applies the improved genetic algorithm to solve job shop scheduling problem, and acquires a satisfied result finally; it studies the application of rough sets theory and function s-rough sets (singular rough sets) theory in job shop dynamic scheduling. Under dynamic processing environment, when some real affairs occur, we should re-schedule the job in the rolling window, as static scheduling can't adapt a dynamic processing environment well. We study the job identification and job scheduling problems in the rolling window under dynamic processing environment. The corresponding job identification methods of the job shop dynamic scheduling window based on rough sets theory and are expert experience presented. We establish the job shop dynamic scheduling modeling based on these theories, and resolve these models using the improved genetic algorithm. The simulation results demonstrate the new methods have certain merits in decrease the re-scheduling degree and improve the system stability compared with the traditional dynamic scheduling methods.The main study works of this dissertation are as follows.1. Firstly, we present the concept and significance of job shop scheduling, and establish the general job shop static and dynamic scheduling models. Improve the genetic algorithm to solve the models. The simulation results show the availability of the improved algorithm.2. Secondly, the job shop dynamic scheduling modeling based on rough sets theory is studied. Based on the approximate characteristic of rough sets, a job identification method of the job shop dynamic rough scheduling window is presented, a decision table is invited and the job shop dynamic rough scheduling model based on rough sets is established. The simulation results demonstrate the advantage of the proposed methods compared with the traditional dynamic scheduling methods. Through the use of this dynamic scheduling algorithm, firstly the dynamic processing environment is adapted and a satisfied scheduling result is obtained, secondly, the problem dimension is reduced and the utility ratio of the equipment is advanced, finally the rescheduling degree is decreased and the system stability is improved.3. Thirdly, we study the application of Function S-rough sets theory in the job shop dynamic rough scheduling compared with the using of S-rough sets. Point out the shortage of S-rough sets, prove the accuracy of the application of Function S-rough sets; this is the biggest innovation point in this dissertation.Finally, the main works of this dissertation are summarized, and the further study directions are pointed out.