基于复杂网络的生产车间瓶颈预测方法研究

发布时间：2018-04-10 07:41

本文选题：生产车间　切入点：瓶颈辨识　出处：《新疆大学》2017年硕士论文

【摘要】：现代生产车间生产模式趋向于柔性化生产,生产订单多为小批量多品种,生产过程和生产环境也越来越复杂,复杂的生产过程和生产车间中存在诸多不确定因素使得瓶颈辨识愈加困难。针对生产车间生产过程中的瓶颈辨识问题,将复杂网络相关理论引入到生产车间生产过程,根据生产车间的工作特点、资源流动、工艺约束、信息流动和人员配置等特征,采集数据并提取关键节点,构建复杂网络模型,实现对生产车间生产过程的网络化数学描述。针对生产车间生产过程多扰动因素,将生产车间生产过程多扰动因素进行分类细分,依据层次分析法、专家打分法、模糊评判法等方法对生产车间生产过程中的扰动因素进行分析与评价。定义扰动因素强度,构建扰动因素强度矩阵,实现对扰动因素的度量,根据历史数据和专家打分法构建状态概率转移矩阵,基于马尔科夫方法预测关键扰动因素,依据扰动因素发生概率对关键扰动因素实施重点管理与监控。在扰动环境下,充分分析扰动因素的作用机理,建立贴近车间生产实际的传播动力学模型,分析扰动因素对生产车间生产过程的影响机制,建立扰动环境下生产车间瓶颈识别与预测模型。根据生产车间的工作特点、资源流动、工艺约束、信息流动和人员配置等特征,构建复杂网络模型实现对生产车间生产过程的网络化数学描述,选取定义网络统计参数;对扰动因素进行数学建模,选取单一扰动因素,构建该扰动因素在网络系统中的作用机理,建立CML(耦合映像格子)的生产车间节点状态预测模型,分别对扰动发生前后节点状态的对比及扰动对节点状态的预测分析,构建基于节点状态与网络特征参数的瓶颈判定机制,依据瓶颈判别机制动态预测瓶颈节点,并进行瓶颈的多态性分析。最后基于ARENA仿真软件结合车间实际生产数据进行仿真实验验证,仿真实验验证了该模型的准确性与合理性。为企业提高生产效率、减少资源浪费、制定生产计划、生产调度、企业管理等提供了理论依据。
[Abstract]:The production mode of modern production workshop tends to be flexible, the production order is mostly small batch and variety, and the production process and production environment are becoming more and more complex.It is more difficult to identify the bottleneck due to the complex production process and many uncertain factors in the workshop.Aiming at the bottleneck identification problem in the production process of production workshop, the related theory of complex network is introduced into the production process of production shop. According to the characteristics of the workshop, such as working characteristics, resource flow, process constraints, information flow and staffing, etc.The data are collected and the key nodes are extracted, and the complex network model is constructed to realize the networked mathematical description of the production process in the workshop.In view of the multi-disturbance factors in the production process of the workshop, the multi-disturbance factors in the production process of the workshop are classified and subdivided, and according to the analytic hierarchy process, the expert scoring method is used.The disturbance factors in the process of production are analyzed and evaluated by fuzzy evaluation method.The intensity of disturbance factor is defined, the intensity matrix of disturbance factor is constructed, the measure of disturbance factor is realized, the state probability transition matrix is constructed according to historical data and expert scoring method, and the key disturbance factor is predicted based on Markov method.The key disturbance factors are managed and monitored according to the occurrence probability of disturbance factors.In the disturbed environment, the mechanism of disturbance factors is fully analyzed, the propagation dynamics model close to the actual production of workshop is established, and the influence mechanism of disturbance factors on the production process of workshop is analyzed.The bottleneck identification and prediction model of production shop under disturbed environment is established.According to the working characteristics of production workshop, resource flow, process constraint, information flow and personnel allocation, a complex network model is constructed to describe the production process of the workshop, and the network statistical parameters are selected and defined.The disturbance factor is modeled mathematically, the single disturbance factor is selected, the mechanism of the disturbance factor in the network system is constructed, and the production shop node state prediction model of the CML (coupled Mapping Lattice) is established.The comparison of node state before and after disturbance and the prediction analysis of disturbance on node state are carried out respectively, and the bottleneck judgment mechanism based on node state and network characteristic parameters is constructed, and the bottleneck node is dynamically predicted according to bottleneck discriminant mechanism.The polymorphism of bottleneck was analyzed.Finally, the simulation experiment based on the ARENA simulation software combined with the actual production data of the workshop is carried out, and the accuracy and rationality of the model are verified by the simulation experiment.It provides theoretical basis for improving production efficiency, reducing waste of resources, making production plan, production scheduling and enterprise management.
【学位授予单位】：新疆大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TB497

【相似文献】