Multi-Objective Scheduling Optimization Of Job-shop In Intelligent Manufacturing System

The multi-objective scheduling optimization of the job-shops is very important because of its theoretical and practical significance. The subject is studied in this thesis and several innovations are presented.A new approach is developed combining the genetic algorithm with a bi-directional approach. The critical jobs must be scheduled by the backward algorithm, which ensures the necessary resources allocated to the most important jobs; then the remaining jobs are scheduled with a forward algorithm, which tends to allocate resources where the previous scheduling pass did not use them. The genetic algorithm derives the optimal operations precedence to schedule the operations in the bi-directional scheduling approach. An example of scheduling is given, which proves this method is feasible and efficient.A potent algorithm is presented to address the reduction of makespan and operating cost. Genetic algorithm combined with cerebellar modal articulation controller (CMAC) is applied for job shop problems. The simulation results show that the makespan and operating cost of the optimal schedule vary with their weights. CMAC is trained with the required specimens and the required optimal schedule results are achieved.The multi-resource constraints and multi-objective scheduling problem is investigated. An active heuristic Scheduling method applied for this problem is proposed. The 23 classical job-shop scheduling problems are used to test the approach, and the results show the algorithm is feasible and superior to the procedure of Ponnambalam.Scheduling problem of batch process with the before-arrival setup time is studied. A combining Genetic Algorithm with the modified Giffler and Thompson procedure is presented. The algorithm adopts three strategies to improve productivity. An efficient scheduling algorithm is developed in the dynamic manufacturing systems. A periodic and event-driven rolling horizon scheduling is utilized to adaptation to continuous processing in a changing environment. Examples prove these methods are available and efficient.The study is supported by key project of National Natural Science Foundation, and is authenticated by the specialists of Commission of Science Technology and Industry for National Defenses. The specialists have declared, "achievements of the study are creative and in the lead of the world. The investigation shows static and dynamic scheduling problems on multi-resources constraints are firstly solved in the world".