A Study On Hybrid Evolutionary Algorithm For Flexible Manufacturing Optimization

Flexible Manufacturing System is controlled by a unified information system, material handling system and a set of numerical control processing equipment components, to adapt to the object transformation processing automation machinery manufacturing system (Flexible Manufacturing System), the English abbreviation for the FMS.In today’s competition, we realized how to use computer technology to optimize production scheduling, rapid adjustment of resources, improve equipment utilization, co-ordinate arrangements for the production schedule has become a major issue now facing.This paper studies scheduling problems in flexible manufacturing model and optimization algorithm based on the vehicle will automatically send transport applications in FMS. FMS and the total completion time scheduling problem size and the number of vehicles as the auto-send evaluation algorithm that takes into account the multi-objective optimization problem. And put forward for the Genetic Algorithm (GA) based solution, in GA, we emphasize the genetic algorithm based on priority.Scheduling problems in flexible manufacturing also used to join Particle Swarm Optimization local search method hybrid algorithm, variable neighborhood decline with GA algorithm can improve the solution convergence. GA is used to complete the exploration of the global population, while local search is used for the development of chromosomes. Because the local search can be more issue-based knowledge to improve the quality of the solution, hybrid genetic algorithm is usually better than using only a single operation.