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题名	混合生产自动排产系统研究与实现
作者	荆绿英
学位类别	硕士
答辩日期	2014-05-28
授予单位	中国科学院沈阳自动化研究所
导师	宋宏
关键词	混合生产 生产单元 改进的人工萤火虫群算法 差分操作 自动排产系统
其他题名	Research and Implementation of Hybrid Production Automatic Planning System
学位专业	控制工程
中文摘要	混合生产方式同时具有连续和离散的特性，其生产计划不但要以市场需求来确定，还要根据企业制造资源的实际能力和库存来调整。设计行之有效的自动排产系统，对于提高企业生产效率，增强市场竞争力有着重要的意义。本文以某卷烟企业为例，对混合型生产自动排产进行研究，并设计实现自动排产系统。鉴于实际生产排产优化问题的复杂性、约束性、非线性、多目标等特点，建立合适的排产模型与寻找合适的优化方法是我们要研究的主要问题。 本文首先对卷烟生产过程进行分析，分别确定制丝生产和卷包生产的优化目标和约束条件；然后根据卷烟生产特点将制丝生产分为四个生产单元，卷包生产作为一个生产单元；继而以生产单元作为计划排产的基本单位，分别对制丝生产和卷包生产构建数学模型。 卷包生产排产计划是卷烟生产排产计划的关键部分，拉动制丝计划排产。卷包生产排产问题是一个NP问题，具有非线性的特点，综合比较传统搜索算法与群智能算法的优缺点，选择群智能算法求解此类问题，另外重点分析各种群智能算法的优缺点，最终选用内存开销小，计算速度快的人工萤火虫群算法实现卷包生产排产，为了改善其易于陷入局部最优的特点，引入了差分操作。根据卷包生产的一个案例，使用改进的人工萤火虫群算法完成计划排产，验证了算法的准确性和较快的收敛速度。当卷包生产确定时，烟支的烟丝需求量时间，需求量也随之确定，故选择算法简单，容易实现，且适合解决订货生产类型的倒排序排产算法。 自动排产系统以Visual Studio 2010为开发环境，C#为编程语言，采用ASP.NET编程框架生成WPF功能界面，并使用SQL Server 2008建立数据库，采用客户/服务器(C/S:Client/Server)模式进行开发。
索取号	F273/J73/2014
英文摘要	Hybrid production has the characteristics of both continuous production and discrete production, its production plan is not only depends on market demand to determine, and also bases on the actual capacity of enterprises manufacturing resources and inventory to adjust. So design of effective automatic production planning system has an important significance for improving the enterprise production efficiency and enhancing market competitiveness. The hybrid production automatic planning is learned, and automatic planning system is designed and realized taking a tobacco company as a example. In view of the complex, binding, non-linear, multi-objective and so on of actual production planning, building a suitable planning modeling and seeking an appropriate optimization method are the main problems we have to research. The tobacco production is analyzed firstly to determine the production optimization objectives and constraint conditions of silk production and cigarette packing production respectively. Secondly according to the characteristics of tobacco production, silk production is divided into four production units, while cigarette packing production actions as one production unit. Finally the mathematical model about silk production and cigarette packing production are build respectively, while production unit as the basic unit of planning. Cigarette packing production planning is the core part of the production planning system, and pulls the silk production plan production planning. After the comparison between traditional search algorithm and swarm intelligence algorithms about advantages and disadvantages, the swarm intelligence algorithm is used to solve such problems, because the cigarette packing production planning has the characteristics of NP-Hard and nonlinear. After analyzing the advantages and disadvantages of various algorithms, finally the Glowworm Swarm Optimization algorithm is selected to achieve cigarette packing production planning because of its small memory overhead and high computing speed. In order to improve the characteristics of easy to fall into local optimum, the Glowworm Swarm Optimization algorithm is improved by introducing the difference operation firstly. A case of cigarette packing production planning is used to verify the improved Glowworm Swarm Optimization algorithm that it has the accuracy and faster convergence speed to complete planning. If the demand for cigarette packing production is determined, the demand of cut tobacco is also determined, so flashback production planning algorithm is chose because of its simple, easy to implement, and suitable for solving order type production. Visual Studio 2010 is taken as development environment, C# is taken as programming language, ASP.NET is used as programming framework for generating WPF interface, and SQL Server 2008 is used for establishing databases, Client/Server model is adopted for design and implementation of automatic planning system.
语种	中文
产权排序	1
页码	61页
分类号	F273
内容类型	学位论文
源URL	[http://ir.sia.ac.cn/handle/173321/14786]
专题	沈阳自动化研究所_数字工厂研究室
推荐引用方式 GB/T 7714	荆绿英. 混合生产自动排产系统研究与实现[D]. 中国科学院沈阳自动化研究所. 2014.

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