A Research Of Production Scheduling Algorithm For Lean Production Management System

With the continuous development of China's manufacturing industry and the increasingly fierce international competition environment,Lean Production Management has become more and more important to enterprises.Production scheduling is one of the most important topics of lean production management.Therefore,production scheduling is not only a research hotspot in industry but also in academia.Manufacturing enterprises can shorten the production cycle of products,increase the utilization rate of equipment,and rationally control the inventory of semi-finished products and finished products through reasonable production scheduling.The model of flexible job shop scheduling problem(FJSP)is suitable for many manufacturing enterprises' true situation;at the same time,some scholars have proved that the FJSP problem is an NP-hard problem.Therefore,the study of FJSP not only has important theoretical value,but also has great practical significance for improving the production efficiency and market competitiveness of manufacturing enterprises.This paper studies the existing solutions to the flexible job shop scheduling problem,the precise method is suitable for solving small-scale problems.When faced with large-scale production scheduling problems,it is difficult for the accurate method to get the solution in a reasonable time.Approximate methods have developed rapidly in recent years.Among them,swarm intelligence and evolutionary algorithms have a good effect in solving FJSP.After studying related swarm intelligence and evolutionary algorithms,this paper summarizes the algorithm structure and improvement methods of this kind of algorithm.Based on this,an improved firefly algorithm and an improved genetic algorithm are proposed to solve FJSP.After studying the related papers on swarm intelligence and evolutionary algorithms which are used to solve the FJSP,the structure and key steps of swarm intelligence and evolutionary algorithms are summarized,and also introduce the coding and decoding methods,population initialization criteria,and local search methods used in these papers which are in popular.The principle of the original firefly algorithm and the influence of related parameters on the algorithm are introduced in detail.For the discrete single-objective optimization FJSP,a discrete firefly algorithm based on neighborhood structure of outstanding individuals is proposed.Finally,the algorithm's feasibility and effectiveness are verified by three standard FJSP.The improved genetic algorithm with a new strategy of selecting the next generation population by an index for evaluating population dispersion is proposed to solve the FJSP.The index is also used to control the population distribution.The improved algorithms are applied to the scheduling of gear production workshop.Finally,the relevant modules are designed in the lean production management system.