Application Of Improved Double Chromosomes Genetic Algorithms For Job-Shop Scheduling Problem

Production scheduling, as a key module, the entire production system for advanced management techniques and technology planning, optimization, automation, and the development of computer technology at the core. Effective method of scheduling and optimization technology research and application of advanced manufacturing and raise the efficiency of production is the basis and key .GA is one of the most widely evolutionary computation methods in scheduling optimization has irreplaceable advantages.In view of the typical job-shop scheduling production scheduling (Job Scheduling Problem), I studied an improved double genetic algorithm. The algorithm provided by the redundant memory makes biological changes in the environment should not forget that the knowledge which can demonstrate a greater adaptive environment, dynamic tracking capability. Aiming at the same time over a single algorithm to update the same way, making the process of optimizing the different stages of the emerging convergence of the diversity and the different requirements of the problem is difficult to be taken into account, adaptive crossover and mutation strategies to effectively address the changing requirements of the stage.By double standard genetic algorithm and Genetic Algorithm comparison and job-shop scheduling function tests effectively demonstrate the advantages of an improved algorithm and its use in solving multi-dimensional, dynamic and complex issues had shown excellent performance.Finally, the preparation of test platform for job-shop scheduling, visual way to show double genetic algorithm to improve the characteristics and advantages.