Research On The Applications Of The Batch Production Schedules Based On An Improved PSO

Capacitated lot-sizing problem is a common practical problem in volume production manufacturing and its main objective is to determine the production lot plan that minimizes production costs or maximizes profit.With the rapid progress of social economy,people's expectation of the manufacturing industry is getting increasingly high,and the cost of production becomes one of the key factors for manufacturing enterprises to win market,win customers and earn profits.Considering the cost-saving,this paper adopts particle swarm optimization(PSO)to solve the problem of actual production batch planning.This paper researches the lot-sizing problem based on several common mathematical models.To solve the problem,PSO and its improved arithmetic and the improved PSO are used.From the results,the update rules of PSO make each particle of the particle swarm rapidly approach the optimal particle,which can improve the convergence rate of arithmetic.Meanwhile,due to the mechanism for selecting the superior and eliminating the inferior,Genetic algorithm(GA)can improve the diversity of particle swarm and make it out of local optimal.Hence,the combination of PSO and GA is helpful for the better global convergence and convergence rate of PSO to improve the performance of the algorithm.To improve the drawback of the algorithm that it is difficult to evolve in the late iteration,this work presents an improved strategy for simultaneous evolution of two populations and the exchange of information.In the end,the proposed scheme is used to solve the lot-sizing problem for a practical example with multiple levels resource constraints.The result demonstrates that the proposed scheme in this paper can help to improve the performance of particle swarm optimization algorithm and solve the lot-sizing problem.