Integrated Optimization Of Process Planning And Scheduling Based On Genetic Algorithm

Process planning and workshop scheduling are two important links in the production and manufacturing process.In the traditional hierarchical production management model,in order to be convenient,these two links are often separated independently,which is not conducive to the overall optimization of the production system.In recent years,the overall optimization of production system has attracted the attention of scholars.The integration of process planning and workshop scheduling can realize the flexible control of the manufacturing system and the rational utilization of the resources of the workshop.It has an important theory for improving the feasibility of flexible manufacturing and process planning,improving the overall optimization of the manufacturing system,improving the utilization rate of the equipment,reducing the cost of production and improving the competitiveness of the enterprise market.Most of the scheduling problems(including job shop scheduling problems)are NP difficult,and the problem of process planning and scheduling integration is more complex,which is one of the most difficult combinatorial optimization problems.This article has carried on the preliminary discussion to this.First,we study the integrated optimization problem aiming at minimizing makespan.Considering that the advantage of the nonlinear process planning model is that all the process routes are generated,the optimization space of the workshop scheduling is most satisfied,and the most suitable process route can be found.However,the nonlinear process planning model has a large number of process routes and large storage space,so the integrated optimization of process planning and scheduling is asked.The problem of solving the problem is not conducive to the solution of the problem.The concept of pseudo artifacts is proposed to reduce the complexity of the description of the process route,and the working procedure can be determined by the traction operator,which is convenient for the genetic algorithm to solve the secondary problems.Secondly,considering the delivery time requirement of processing tasks,the integrated optimization problem with the goal of minimizing lead time and tardiness is considered.For these two problems,the optimization models are established respectively,and the solution method based on genetic algorithm is given,and the effectiveness of the algorithm is verified by simulation.