Research On Production Planning And Production Control Method For Electronic Manufacturing Industry Under Quasi-Assemble-to-order Environment

In recent years, assemble-to-order (ATO) is an advanced production organization method.By this method, enterprise can provide customized product with low cost in short delivery time. ATO is widely adopted by abroad enterprise.ATO has great potential in the future. Sponsored by the Key Sci. & Tech. Program of Zhejiang Province, China (No.2003C 11010, NO.2005C11034), based on in-depth analysis of the ATO operation process, this dissertation focused on the production planning and control. That provided a solid theoretical basis and guidance method for the successful implementation of ATO and rapid response to market demand and customer orders.In chapter one, the background and significance of research paper is stated, the research state at home and abroad related to ATO is summarized, and the research objective, and presented the main research content and paper structure is presented.In chapter two, based on the analysis of characteristics of electronic manufacturing enterprise and the analysis of the ATO mode, the quasi-assemble-to-order mode is proposed. The four-layered implementation method system is put forward based on the comparison between ATO and quasi-ATO and three key problems that this thesis focuses on are analyzed in detail. In the end,the demand forecast techniques based on support vector machine (SVM) is proposed and the chaos particle swarm optimization algorithm is put forward to solve the parameter selection problem.In chapter three, a master planning schedule based on storing products and parts for fast delivery was brought forward. A fuzzy production planning model based on credibility programming under fuzzy environment was constructed and transformed into the form of a clear equivalent through the clarity of fuzzy objectives and constraints. A modified PSO algorithm based on coevolutionary and infeasibility was given for solving this model.In chapter four, we focus on the production control of component manufacture.Firstly, since the process time is random, its distribution is calculated by regression method. Secondly, a stochastic programming model is built to find the optimum initial shop floor production planning and an integrated intelligent algorithm that based on Monte-Carlo simulation, particle swarm optimization and SVM is proposed to solve the model. Thirdly, an integrated shop floor dynamic scheduling framework was introduced. This framework includes a scheduling drive mechanism and a dynamic scheduling method which was based on the real-time information of shop floor. Since the scheduling method is based on machine learning, the main problem is scheduling feature selection. Finally we put forward an immune binary particle swarm optimization to select the appropriate features。 Scheduling under ATO environment is a muliti-product,multi-order and multi-resources combinatorial optimization problems.In chapter five, the sequence of scheduling orders was given by calculating order priority value based on evaluation orders priority indicators and fuzzy comprehensive appraisement model which using entropy weight. The assembly scheduling model was established. A new adaptive particle swarm optimization algorithm with dynamically changing inertia weight (DCWPSO) was brought forward to solve the problem.In chapter six, the main conclusions of this dissertation are summarized and the further research issues are put forward.