Research On Modeling And Optimization For Production Scheduling Of Garment Manufacturing System

Garment industry is a traditional advantageous industry,an important pillar industry and people’s livelihood industry in China.It plays an important role in improving people’s living standards,prospering the national economy and promoting social progress.With the development of society and the progress of science and technology,more and more garment enterprises use the idea and method of computer integrated manufacturing to organize and manage the manufacturing system.Production scheduling plays an important role in computer integrated manufacturing system.It is one of the key technologies to achieve high efficiency,high flexibility and high reliability of manufacturing activities.It is also an indispensable part of enterprise production management.To carry out the research on the modeling and optimization for the production scheduling problem of garment manufacturing system,and expand the production scheduling problem from the strictly limited ideal environment to the real garment production environment is conducive to improve the modeling accuracy and optimization efficiency for the production scheduling decision of garment manufacturing system,it can improve the rationality of resource allocation and production order in the process of garment production.Based on production scheduling theory and optimization theory,the dissertation focuses on solving the backward and poor practicability of traditional garment production scheduling methods.Aiming at the problems extracted from the actual garment production management,such as the formulation of sewing standard man hours,the production scheduling of single-stage multi-machine garment manufacturing system,the production scheduling of multi-stage single-machine garment manufacturing system and the production scheduling of multi-stage multi-machine garment manufacturing system,the model construction and algorithm research are systematically and deeply studied.The main research contents are as follows:The method of grounded theory is used to explore the influencing factors of garment production scheduling.According to the procedures of open coding,spindle coding and selective coding,47 concepts are extracted,12 categories and 4 main categories are summarized.The action mechanism model of influencing factors of garment production scheduling is established.On this basis,the technical framework of garment production scheduling is constructed,which includes five layers: target layer,criterion layer,variable layer,scheme and technology layer and influencing factor layer.Standard working hours are the basic data for garment manufacturers to prepare production scheduling plans and improve labor efficiency.With the continuous development and application of advanced manufacturing technologies such as computer integrated manufacturing and intelligent manufacturing in the garment industry,garment enterprises have higher requirements for the accuracy and efficiency method of standard working hours.Sewing production stage is an important part of garment production,accounting for a large proportion of garment production cost and time.Aiming at the production scheduling optimization of garment manufacturing system,this dissertation puts forward the preprocessing research: the research of garment sewing standard man hours.Based on the analysis of the main influencing factors of the standard working hours of sewing process,the formulation model of standard working hours for garment sewing process based on particle swarm optimization support vector machine is established to determine the standard working hours of sewing process quickly and accurately.Production scheduling problem of single-stage multi-machine garment manufacturing system.Select the garment sewing production stage and minimize the makespan as the optimization goal,build an unrelated parallel scheduling model with the sequence related setup time.An improved genetic simulated annealing hybrid algorithm is proposed to solve the problem.According to the new population generated by each generation of genetic operation,simulated annealing algorithm is used to optimize one by one,which not only retains the advantages of global search of genetic algorithm,but also integrates the advantages of local search of simulated annealing algorithm to improve the solution accuracy of the algorithm.Simulation results show that the proposed algorithm has higher accuracy and better global optimization ability,which proves that the improvement of the algorithm is effective.Production scheduling problem of multi-stage single-machine garment manufacturing system.Establish integrated production scheduling and preventive maintenance model.An improved harmony search algorithm is designed.As an optimization algorithm to solve the problem,the newly generated harmony is disturbed by introducing domain search operators such as insertion,reverse order and exchange,so as to improve the harmony diversity level in the harmony memory,which is conducive to the algorithm jumping out of the local optimal solution.The effectiveness of the algorithm is verified by applying production data.Production scheduling problem of multi-stage and multi-machine garment manufacturing system.Considering the sequence dependent setup time and the constraints of unrelated parallel production system in the production process,a hybrid flow scheduling model for multi-stage and multi-production system is established.For this model,an improved two population genetic algorithm is proposed to solve it.Sub population 1,as the development population,focuses on improving the exploitation performance of the algorithm,and sub population 2,as the detection population,focuses on improving the exploration ability of the algorithm.In the process of evolution,the specific individuals of the two populations are exchanged to realize collaborative optimization.The results show that the proposed algorithm is suitable for solving the production scheduling problem of multi-stage and multi machine garment manufacturing system.