面向制造车间的生产优化调度与执行技术研究及应用

发布时间：2018-03-13 09:18

本文选题：生产调度　切入点：粒子群算法　出处：《江苏科技大学》2016年硕士论文　论文类型：学位论文

【摘要】：日益多变的市场需求使得制造型企业车间需要应对更加复杂的生产任务,这对生产作业计划的制定和执行都提出了较高的要求。然而很多中小企业的制造车间仍采用人工加经验的车间管理方式,己经难以满足企业的现代化发展需要。因此,本课题针对某换热器企业制造车间存在主要问题和实际需求,对基于蛙跳混合粒子群算法的车间生产调度技术、针对生产系统瓶颈约束的缓冲技术、快速响应制造执行过程协同技术等关键技术进行研究和应用,并以此为基础开发集成化生产管理系统,实现最优生产作业计划的制定、优化和调整,以及系统各功能模块之间的快速响应制造执行过程协同,从计划制定和制造执行这两方面来提高企业车间的生产效率和生产管理水平。本文主要研究内容如下:(1)针对车间生产排程问题,构建了一种融合蛙跳算法及遗传算法的改进型粒子群算法,对生产排程数学模型进行多目标优化求解,该算法具有较好的寻优性能,能帮助计划调度人员快速制定出最佳的生产作业计划,提高生产效率。同时,对面向生产扰动事件的车间动态调度方法进行了研究,使生产作业计划能得到最有效的调整。(2)针对生产计划中的瓶颈工序,研究了最佳时间缓冲量和最佳库存缓冲量的计算和修正方法,并通过工程实例检验了该瓶颈缓冲技术及其计算方法的可行性。该瓶颈缓冲技术能够进一步优化生产作业计划,提高生产系统稳定性。(3)针对制造车间生产作业计划执行能力较差的问题,通过对复杂信息的关联组织,建立了信息快速响应处理机制,并设计了生产管理系统的各功能模块和集成运行模式,以实现全过程、多方位、多角色的快速响应制造执行过程协同,提高生产作业计划的执行效率,提高生产效率。(4)开发了集成化生产管理系统平台,实现了生产计划管理子系统对生产排程、动态调度、瓶颈缓冲等子模块的集成,并通过各功能模块子系统之间的集成运行,实现快速响应制造执行过程协同。
[Abstract]:The ever-changing market demand makes the workshop of manufacturing enterprises need to deal with more complex production tasks. However, many small and medium-sized enterprises still adopt manual and experienced workshop management mode, which is difficult to meet the needs of modern development of enterprises. Aiming at the main problems and actual demand of a heat exchanger enterprise manufacturing workshop, this paper discusses the production scheduling technology based on breaststroke hybrid particle swarm optimization algorithm, and the buffer technology of bottleneck constraint in production system. Based on the research and application of key technologies such as rapid response manufacturing execution process cooperation technology, an integrated production management system is developed to make, optimize and adjust the optimal production schedule. And the rapid response manufacturing execution process cooperation among the functional modules of the system, In order to improve the production efficiency and production management level of the enterprise workshop from the two aspects of planning and manufacturing execution, this paper mainly studies as follows: 1) aiming at the problem of workshop production scheduling, An improved particle swarm optimization (PSO) algorithm combining leapfrog algorithm and genetic algorithm is constructed to solve the mathematical model of production scheduling with multi-objective optimization. It can help the scheduler to make the best production plan quickly and improve the production efficiency. At the same time, the dynamic scheduling method for production disturbance event is studied. (2) aiming at the bottleneck process in the production plan, the calculation and correction method of the optimum time buffer and the best inventory buffer are studied. Finally, the feasibility of the bottleneck buffer technology and its calculation method is verified by an engineering example. The bottleneck buffer technology can further optimize the production schedule. To improve the stability of production system, aiming at the problem of poor execution ability of production scheduling in manufacturing workshop, a mechanism of information rapid response processing is established through the association organization of complex information. The functional modules and the integrated operation mode of the production management system are designed to realize the cooperation of the whole process, multi-position and multi-role rapid response manufacturing execution process, and to improve the efficiency of the production plan execution. The platform of integrated production management system is developed to realize the integration of production scheduling, dynamic scheduling, bottleneck buffer and other sub-modules of production planning management subsystem. To achieve rapid response manufacturing execution process collaboration.
【学位授予单位】：江苏科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TB497

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