基于BA-BP算法的汽车配件需求预测系统研究与实现

发布时间：2018-01-28 11:17

本文关键词： 蝙蝠算法 BP神经网络汽车配件需求预测　出处：《西南交通大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着汽车产业的迅速发展,企业面临着更为复杂的环境和更为强劲的竞争对手。因此,汽车企业不仅仅需要提升自身的制造技术,更要提高售后服务质量。想要更稳定,更优质的售后服务,汽车配件库存量相对就要增多,但库存量的增多,带来的就是成本增加,库存过剩的风险加大。因此,有效的、准确的汽车配件需求预测不仅能有效降低库存的成本,还能提高汽车售后服务的质量,使企业获得更大的利益。首先,本文致力于设计并实现一个合理的汽车配件需求预测系统。以制造厂为核心,通过对汽车配件供应链和主要业务流程的研究,寻找影响汽车配件需求的关键因素。根据实际的调研需求,对比国内外汽车配件预测方案,选择由BA(蝙蝠算法)优化BP神经网络的方式来建立预测模型,并通过模型的训练和测试结果来验证该模型的有效性。其次,预测的基础在于数据,传统的配件需求预测往往只针对本地的数据进行预测,而忽视整个配件供应链上各节点数据之间的相互影响,这样往往会导致需求预测不准确。本系统利用JAX-WS框架来开发WebService,结合RSA加密算法来实现系统与企业之间安全的数据交换,并用整合过来的数据进行模型训练,提高模型预测的准确度。最后,根据系统需求实现系统管理模块、基础数据查询模块、数据交换模块、配件预测查询模块和模型控制模块,并完成对系统的功能测试和性能测试,验证系统的准确性和稳定性。本文将数据交换技术、预测技术和实际需求相结合,提高了预测的准确度和系统的可行性,并向用户提供简单、明了的可视化界面及多维度图表的展示,让用户能更直观的了解数据,为其后期的需求决策提供帮助。
[Abstract]:With the rapid development of automobile industry, enterprises are faced with more complex environment and more powerful competitors. Therefore, automobile enterprises not only need to improve their manufacturing technology. More to improve the quality of after-sales service. Want more stable, more high-quality after-sales service, automotive spare parts inventory will be relatively increased, but the increase in inventory, it is cost increase. Therefore, effective and accurate demand prediction of auto parts can not only effectively reduce the cost of inventory, but also improve the quality of automobile after-sales service, so that enterprises can obtain greater benefits. This paper is devoted to design and implement a reasonable demand forecasting system for auto parts, taking the manufacturer as the core, through the research of the automobile parts supply chain and the main business process. In order to find the key factors that affect the demand of automobile parts, according to the actual research demand, comparing the domestic and foreign prediction schemes of auto parts, we choose the method of optimizing BP neural network by BA( bat algorithm) to build the prediction model. And through the model training and testing results to verify the effectiveness of the model. Secondly, the basis of the prediction is data, the traditional forecasting of accessories demand often only for the local data forecast. But ignore the interaction between the data of each node in the whole spare parts supply chain, which often lead to inaccurate demand prediction. This system uses JAX-WS framework to develop WebService. RSA encryption algorithm is used to realize the secure data exchange between the system and the enterprise, and the integrated data is used to train the model to improve the accuracy of model prediction. The system management module, basic data query module, data exchange module, accessory prediction query module and model control module are implemented according to the system requirements, and the function test and performance test of the system are completed. This paper combines data exchange technology, prediction technology and actual demand to improve the accuracy and feasibility of the system, and provide users with simple. The clear visual interface and the display of multi-dimensional charts enable users to understand the data more intuitively and provide help for their later demand decisions.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.52

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