Research On Integrated Process Planning And Scheduling Problem Based On Hybrid Genetic Algorithm

In today's highly competitive market and corporate environment,process planning and scheduling are two important functions in a manufacturing system.The traditional manufacturing system first produces an overall process plan,and then schedules the parts and operations on the basis of the process plan.However,most of the process planning is based on a single objective to select the process route for each part,and only select the ideal resources.Besides,multi-target scheduling is more in the actual production environment,problems,such as confliction of goals,machine overloading and infeasibility of process plans,have promoted research on the Integrated Process Planning and Scheduling(IPPS)problem.Therefore,the application of efficient technologies to promote the optimization of IPPS has constructive significance for the improvement of productivity and competitiveness.The object of this research is the description model and implementation optimization of IPPS problem.Firstly,the research background and significance of the research on IPPS problem are analyzed.The overall research content,structure of the paper and technical route are proposed.The definition,research status and related issues of IPPS are summarized,especially description model for parts and implementation of this type of problem.Besides,the principles and applications of the genetic algorithm and simulated annealing algorithm in the study are also showed.Then,the concepts of "Macro Operation" and "flexibility primitive" have been proposed.Precedence constraints and OR subgraphs of the flexibility primitive are applied to describe the sequencing flexibility and routing respectively.IPPS description model and a mixed integer programming model to integrate process planning and scheduling functions were designed based on the these two concepts.In addition,a hybrid GASA hybrid algorithm was established.Finally,IPPS benchmark experiments of different scales verify the effectiveness of the description model and the algorithm.Through comparison between experiment results of our study and others,it can be concluded that the GASA hybrid algorithm proposed in this study can achieve good results,which also verifies the versatility and completeness of the description model.