本文關(guān)鍵詞： 備件消耗量預(yù)測 概率統(tǒng)計分析法 MapReduce 模糊綜合評價法　出處：《內(nèi)蒙古科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：某煤礦集團公司成功的引進了SAP公司的ERP（Enterprise Resource Planning）系統(tǒng)，ERP系統(tǒng)的使用給企業(yè)帶來了先進的管理理念，建設(shè)完成了完整的企業(yè)資原管理體系和高效、便捷的信息技術(shù)平臺。但是，，上述ERP系統(tǒng)分析和計算備件的儲備定額側(cè)重于機械制造等備件消耗規(guī)律性較強的行業(yè)，對于煤礦企業(yè)這類備件消耗隨需求變化的行業(yè)起不到應(yīng)有的作用，所以開發(fā)了備件儲備定額系統(tǒng)來對備件信息進行管理，協(xié)助業(yè)務(wù)人員制定備件采購計劃，自動提示所需訂貨的備件等。但是，隨著系統(tǒng)的使用，一些問題也緊跟著暴露出來。如儲備定額系統(tǒng)對于日常少量備件做消耗量預(yù)測可以在較短的時間內(nèi)很好的完成，可在年中需要為下半年做訂購計劃或為來年制定訂購計劃的時候，因為其備件庫的龐大（現(xiàn)常用備件有29萬多種，歷史出入庫存記錄數(shù)據(jù)更多），做消耗量預(yù)測需要花費很長的時間；另外備件儲備定額模型中緩沖存儲量計算中用到了裕度系數(shù)，儲備定額系統(tǒng)中裕度系數(shù)的選取是采購人員人為確定的，沒有采用科學(xué)的方法從訂貨周期內(nèi)的生產(chǎn)計劃、往年同期的備件消耗量、備件的供應(yīng)情況等因素綜合確定，這樣的后果是主觀因素大，影響準(zhǔn)確性。因此怎樣有效的計算出預(yù)測值制訂訂貨計劃，以及提出一個裕度系數(shù)的確定方法是本文所要解決的問題。 隨著Hadoop云計算平臺在各個領(lǐng)域的運用很好的證明了其對海量數(shù)據(jù)的存儲能力和并行計算能力，這為解決大量備件的消耗量預(yù)測提供了一種新的解決方式，本文提出基于Hadoop云計算平臺的備件消耗量預(yù)測系統(tǒng)。該系統(tǒng)分為數(shù)據(jù)獲取模塊、數(shù)據(jù)存儲模塊、數(shù)據(jù)預(yù)處理模塊和備件消耗量預(yù)測模塊四部分。其中，數(shù)據(jù)獲取模塊利用某煤炭集團公司的ERP系統(tǒng)Web Service接口來獲取用戶數(shù)據(jù)；數(shù)據(jù)存儲模塊中將數(shù)據(jù)獲取模塊中獲取的備件數(shù)據(jù)按設(shè)計的數(shù)據(jù)格式存入本地Oracle數(shù)據(jù)庫中；數(shù)據(jù)預(yù)處理模塊利用VS2010開發(fā)程序?qū)?shù)據(jù)庫中備件數(shù)據(jù)按要求進行處理，得到我們需要的數(shù)據(jù)格式的數(shù)據(jù)，通過多層次模糊綜合評價法，從備件的關(guān)鍵性和備件所屬設(shè)備的關(guān)鍵性兩方面對備件重要性進行評價，量化備件重要性得到所需的裕度系數(shù)K；備件消耗量預(yù)測模塊中對備件消耗量預(yù)測方法（概率統(tǒng)計分析法）進行改進，以經(jīng)典矩陣相乘的經(jīng)典算法為基礎(chǔ)，利用MapReduce編程框架進行MapReduce化設(shè)計，構(gòu)建MapReduce并行處理算法并在MapReduce并行編程模型上實現(xiàn)。實驗結(jié)果表明，經(jīng)過MapReduce設(shè)計的算法在處理器的可擴展性、數(shù)據(jù)的可擴展性和加速比性能這三方面的實驗中具有良好的指標(biāo)，算法性能表現(xiàn)良好。
[Abstract]:A coal mine group company successfully introduced the ERP(Enterprise Resource planning system of SAP Company to bring the advanced management idea to the enterprise, and completed the complete enterprise capital original management system and the efficient and convenient information technology platform. The above ERP system analysis and calculation of spare parts reserve quota is focused on the industries with strong regularity of spare parts consumption, such as mechanical manufacturing, which does not play a due role in the industries where the consumption of spare parts varies with the demand of coal mining enterprises. So the spare parts reserve quota system has been developed to manage the spare parts information, to assist the business personnel to draw up the spare parts purchase plan, to automatically prompt the spare parts that need to be ordered, etc. However, with the use of the system, Some problems have come to light. For example, the reserve quota system can predict the consumption of a small amount of spare parts in a short time. When you need an order plan for the second half of the year in the middle of the year, or an order plan for the coming year, because of the size of its spare parts warehouse (there are now more than 290,000 commonly used spare parts, In addition, the margin coefficient is used in the calculation of buffer storage capacity in spare parts reserve quota model. The selection of margin coefficient in the reserve quota system is artificially determined by the purchasing personnel, and no scientific method is used to determine the production plan in the order cycle, the consumption of spare parts in the same period of previous years, the supply of spare parts, and so on. Therefore, how to calculate the forecast value effectively to make the order plan, and to put forward a method of determining the margin coefficient is the problem to be solved in this paper. With the application of Hadoop cloud computing platform in various fields, it has proved its storage capacity and parallel computing ability of massive data, which provides a new solution to solve the consumption prediction of a large number of spare parts. This paper presents a spare parts consumption prediction system based on Hadoop cloud computing platform. The system is divided into four parts: data acquisition module, data storage module, data preprocessing module and spare parts consumption prediction module. The data acquisition module uses the Web Service interface of the ERP system of a coal group company to obtain the user data, and the data storage module stores the spare parts data obtained from the data acquisition module into the local Oracle database according to the designed data format. The data preprocessing module uses the VS2010 development program to process the spare parts data in the database according to the requirement, and obtains the data of the data format that we need, through the multi-level fuzzy comprehensive evaluation method, The importance of spare parts is evaluated in terms of the criticality of the spare parts and the criticality of the equipment to which the spare parts belong, The margin coefficient K is obtained by quantifying the importance of spare parts, and the prediction method of spare parts consumption (probabilistic and statistical analysis) is improved in the prediction module of spare parts consumption, which is based on the classical algorithm of multiplying the classical matrix. The MapReduce parallel processing algorithm is constructed and implemented on the MapReduce parallel programming model using the MapReduce programming framework. The experimental results show that the algorithm designed by MapReduce is extensible in the processor. The experiments of data scalability and speedup performance have a good performance, and the performance of the algorithm is good.
【學(xué)位授予單位】：內(nèi)蒙古科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP311.13;F224;F426.21

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