Material Cutting Optimization Research Based On Genetic Algorithm

With the rapid development of social economy, under the tremendous pressure of enterprise competition, reducing the enterprise's cost and designing reasonable raw material processing schemes are important means to improve enterprise's value. To date, most enterprises process raw materials according to the artificial experience, but the uncertainty of designed schemes often cause huge waste of raw material, increasing the enterprises' cost. The research and application of computer algorithm provide an effective way for enterprise to solve the problem, reducing the enterprise's manual investment. At the same time,making up the defect of the design experience.In this paper, for the calculation and optimization of the raw material cutting plan in the enterprise,author mainly finished the following jobs:(1)Put forward the material cutting problems and their evaluation methods in the process of enterprise production,thus paper converted the actual demand into mathematical formula,got the mathematical models by analyzing the problems,and inductived the problems' conditions,put forward the specific algorithms.(2)Based on the physical foundation of simulated annealing algorithm,this paper established a basic mathematical model of the algorithm,according to the algorithm thought, and extracted the core designs and introduced the selecting principle of algorithm's parameters in detail and the influences.In combination with economic demands,this paper put the minimum excess stock in material cutting as a purpose of searching,by the investigation and statistics,the parameters setting and the design of functions were determined by experiments,and ensured the operational process by the algorithm thoughts.Based on the material weight and weight requirements,this paper designed the algorithm program,and tested the production data,compared the experimental results and the solutions,analyzed the advantages and disadvantages of simulated annealing algorithm performance and scheme of minimum remaining material.(3)Based on the ideas of genetic algorithm,this paper set up the basic mathematical models of individual genes and genetic manipulations,and analysed the flow chart of biological genetics,extracted the kernel design of genetic algorithm,and explained its importance.The genetic operations of genetic algorithm were analysed carefully,and investigated the selecting principle and interval of genetic operators.Combining with the production requirements, for the purpose of requirements,this paper designed the size of the population and the objective function,and ensured the parameters of the algorithm,selected specific methods of genetic operation.Ensured the thought process of genetic algorithm,demand quantity and material specifications were considered as experimental conditions,author developed experimental procedure of the algorithm,test the production data.Compared the experimental results and the actual solution,based on the excess stock of experimental and the excess stock of simulated annealing algorithm,author analyzed the advantages and disadvantages of genetic algorithm performance and production quantities.(4)According to the experimental results of simulated annealing algorithm and genetic algorithm,this paper put forward a improved genetic algorithm that combined the advantages of both algorithms,and based on the genetic algorithm,integrated the advantage of simulated annealing algorithm in local search,author designed the idea of improved algorithm.The parallelism of genetic algorithm provides the simulated annealing algorithm with convergence speed,and for the disadvantages of redundancy in simulated annealing algorithm,genetic algorithm provides more abundant historical information,and simulated annealing algorithm enhances the performance of the genetic algorithm in local search at the same time in order to avoid premature convergence.The improved algorithm mixed simulated annealing algorithm and genetic algorithm can weaken the dependence of extrinsic parameters,to ensure the high-efficiency of improved genetic in global search.