Study Of Job Shop Dynamic Scheduling Based On Rough Sets Theory

This dissertation studies the application of rough sets theory and S-rough sets ( singular rough sets ) theory in job shop dynamic scheduling. We study the job re-identification and job re-scheduling problems in the job shop dynamic scheduling window under dynamic processing environment. The corresponding job identification methods of the job shop dynamic scheduling window are presented, in the case of the machine failure and repair, the due data change of jobs and the urgent jobs coming. The new methods are the organic combinations of rough sets or S-rough sets, the mathematics programming and the scheduling expert experience. We establish the job shop dynamic scheduling modeling based on these theories, and resolve these models using an effective genetic algorithm. The simulation results demonstrate the novel methods compared with the traditional dynamic scheduling methods have certain merits in the decrease of the re-scheduling degree and the improvement of the system stability.The main study works of this dissertation are as follows.1. At first, we give the concept and significance of job shop scheduling, and establish the general job shop static and dynamic scheduling models. The algorithm cross operators of the genetic algorithm is changed for the characteristic of job shop scheduling and the real time demand of job shop dynamic scheduling. The simulation results show the availability and correctness of the proposed algorithm.2. Secondly, as a novel application of rough sets theory, the job shop dynamic scheduling modeling is studied. Based on the approximate characteristic of rough sets, a job identification method of the job shop dynamic rough scheduling window is presented, the correlation conceptions of job shop dynamic rough scheduling are defined, and the job shop dynamic rough scheduling model based on rough sets is established. The simulation results demonstrate the correctness and 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 genetic algorithm in the jobshop dynamic rough scheduling, and present a dynamic rough scheduling algorithm based on genetic algorithm. The simulation results show the availability of the proposed algorithm.4. As follow, as another novel application of S-rough sets theory based on its assistant set, its dynamic approximate characteristic and its element transfer characteristic, the job shop dynamic scheduling modeling is studied. The scheduling expert experiences are effectively applied to the model parameter setting and the relative attribute value setting of rough sets. A job identification method of the job shop dynamic S-rough scheduling window is presented, the correlation conceptions of job shop dynamic S-rough scheduling are defined, and the job shop dynamic S-rough scheduling model based on S-rough sets is established. Not only dynamic S-rough scheduling model has the merits of dynamic rough scheduling model, but also it better describes the dynamic characteristic of job shop dynamic scheduling, and its implement is more convenience, more system, and has more theory basis compared with the dynamic rough scheduling model. The simulation results show the correctness and the advantage of the proposed methods. By the use of the dynamic S-rough scheduling algorithm, the rescheduling degree is decreased farther, and the satisfied scheduling result is obtained,5. And then, we study the application of genetic algorithm in the job shop dynamic S-rough scheduling, and present a dynamic S-rough scheduling algorithm based on genetic algorithm. The simulation results show the availability...