基于的煤矿物资计划管理系统的研究
发布时间：2019-06-21 16:31
【摘要】：煤炭作为我国能源消耗的主要来源之一，在经济发展当中具有举足轻重的地位。随着信息技术的不断发展，互联网技术和移动互联网技术的广泛应用，以及经济全球化进程的加剧，使得市场竞争更加激烈。国内的煤炭企业物资计划管理信息化水平整体还比较低，随之带来的问题是库存积压严重，占用大量资金，企业生产成本增大。为实现“零库存”的管理思想，延伸供应链到采煤工作面，做好煤炭企业物资计划的精细化管理，，利用信息技术科学有效地对煤矿生产所消耗的物资进行动态预测、建立物资计划提报的智能化管理系统是降低企业库存成本，提高物资供应链的精细化管理水平的关键手段。 本文首先分析了当前煤炭企业物资计划管理存在的一些问题，然后阐述了物资计划管理的相关内容，根据已经分类好的物资，通过提取煤矿井下生产作业所需的这类物资的影响因素，设计关键指标进行分析，确立了物资需求预测关键指标体系。同时针对支持向量机模型在参数选取时具有一定的主观性和参数优化程度不够的问题，采用粒子群优化算法对支持向量机模型的最佳参数进行最优选取，然后将最优选取的参数结果应用于支持向量机对物资需求预测，以实际的物资采煤机截齿为例进行预测，预测结果表明通过粒子群算法对参数优化后的支持向量机预测模型提高了预测精度。 本论文以实际参与的企业课题“山东能源淄矿集团供应链电子商务系统”为背景，通过分析淄矿集团当前计划管理的业务需求，提出了煤矿企业物资计划管理系统的整体架构，探讨了系统的技术实现方案。对其中的物资预测进行数据建模，选择粒子群优化的支持向量机作为物资需求的预测模型，基于J2EE开发平台，并采用Oracle10g数据库作为底层数据支撑平台，同时以Activiti5流程引擎设计并开发了一套符合淄矿集团实际业务的基于PSO-SVM(Particle Swarm Optimization-Support Vector Machine，PSO-SVM)的物资计划管理平台，实现了淄矿集团物资计划管理的信息化，最后对论文的研究工作做以总结，并对系统的进一步研究做以展望。
[Abstract]:Coal, as one of the main sources of energy consumption in China, plays an important role in economic development. With the continuous development of information technology, the wide application of Internet technology and mobile Internet technology, as well as the aggravation of the process of economic globalization, the market competition is more intense. The information level of material plan management in domestic coal enterprises is still relatively low as a whole, which brings about the problem of serious inventory backlog, occupying a large amount of funds and increasing the production cost of enterprises. In order to realize the management idea of "zero inventory", extend the supply chain to the coal mining face, do a good job in the fine management of the material plan of coal enterprises, make use of information technology to predict the materials consumed in coal mine production scientifically and effectively, and establish the intelligent management system of material plan reporting is the key means to reduce the inventory cost of enterprises and improve the fine management level of material supply chain. This paper first analyzes some problems existing in the material planning management of coal enterprises at present, and then expounds the related contents of the material planning management. According to the classified materials, by extracting the influencing factors of this kind of materials needed in the underground production and operation of coal mine, the key indexes are designed and analyzed, and the key index system of material demand prediction is established. At the same time, in order to solve the problem that the support vector machine model has certain subjectivity and the parameter optimization degree is not enough in the parameter selection, the particle swarm optimization algorithm is used to select the best parameters of the support vector machine model, and then the optimal selection parameter results are applied to the support vector machine to predict the material demand, and the actual material shearer cutting teeth are taken as an example to predict. The prediction results show that the particle swarm optimization algorithm is used to improve the prediction accuracy of the optimized support vector machine prediction model. Based on the actual enterprise project "supply chain Electronic Commerce system of Shandong Energy Zimine Group", this paper analyzes the business requirements of the current planning management of Zimine Group, puts forward the overall structure of the material planning management system of coal mining enterprises, and probes into the technical realization scheme of the system. The data modeling of material prediction is carried out, and the support vector machine of particle swarm optimization is selected as the prediction model of material demand, based on J2EE development platform, and Oracle10g database is used as the underlying data support platform. At the same time, a set of material planning management platform based on PSO-SVM (Particle Swarm Optimization-Support Vector Machine,PSO-SVM is designed and developed with Activiti5 process engine. Finally, the research work of the paper is summarized, and the further research of the system is prospected.
【学位授予单位】：西安科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP311.52;F426.21;F251
本文编号：2504215
[Abstract]:Coal, as one of the main sources of energy consumption in China, plays an important role in economic development. With the continuous development of information technology, the wide application of Internet technology and mobile Internet technology, as well as the aggravation of the process of economic globalization, the market competition is more intense. The information level of material plan management in domestic coal enterprises is still relatively low as a whole, which brings about the problem of serious inventory backlog, occupying a large amount of funds and increasing the production cost of enterprises. In order to realize the management idea of "zero inventory", extend the supply chain to the coal mining face, do a good job in the fine management of the material plan of coal enterprises, make use of information technology to predict the materials consumed in coal mine production scientifically and effectively, and establish the intelligent management system of material plan reporting is the key means to reduce the inventory cost of enterprises and improve the fine management level of material supply chain. This paper first analyzes some problems existing in the material planning management of coal enterprises at present, and then expounds the related contents of the material planning management. According to the classified materials, by extracting the influencing factors of this kind of materials needed in the underground production and operation of coal mine, the key indexes are designed and analyzed, and the key index system of material demand prediction is established. At the same time, in order to solve the problem that the support vector machine model has certain subjectivity and the parameter optimization degree is not enough in the parameter selection, the particle swarm optimization algorithm is used to select the best parameters of the support vector machine model, and then the optimal selection parameter results are applied to the support vector machine to predict the material demand, and the actual material shearer cutting teeth are taken as an example to predict. The prediction results show that the particle swarm optimization algorithm is used to improve the prediction accuracy of the optimized support vector machine prediction model. Based on the actual enterprise project "supply chain Electronic Commerce system of Shandong Energy Zimine Group", this paper analyzes the business requirements of the current planning management of Zimine Group, puts forward the overall structure of the material planning management system of coal mining enterprises, and probes into the technical realization scheme of the system. The data modeling of material prediction is carried out, and the support vector machine of particle swarm optimization is selected as the prediction model of material demand, based on J2EE development platform, and Oracle10g database is used as the underlying data support platform. At the same time, a set of material planning management platform based on PSO-SVM (Particle Swarm Optimization-Support Vector Machine,PSO-SVM is designed and developed with Activiti5 process engine. Finally, the research work of the paper is summarized, and the further research of the system is prospected.
【学位授予单位】：西安科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP311.52;F426.21;F251
【参考文献】
相关期刊论文 前1条
1 李建民,张钹,林福宗;支持向量机的训练算法[J];清华大学学报(自然科学版);2003年01期
本文编号：2504215
本文链接：https://www.wllwen.com/guanlilunwen/gongyinglianguanli/2504215.html
