本文選題：離散制造業(yè) + 預(yù)測�。� 參考：《寧夏大學(xué)》2017年碩士論文

【摘要】：為了實現(xiàn)對企業(yè)的信息進行動態(tài)管理,企業(yè)一般采取高級計劃排程(Advanced Planning and Scheduling)方法。這是一種可將時間、訂單、庫存、預(yù)測產(chǎn)量等生產(chǎn)中的重要因素考慮進去,在企業(yè)生產(chǎn)過程中隨時獲取各種動態(tài)變化從而調(diào)整生產(chǎn)去迎合市場變化的方法。它解決了企業(yè)產(chǎn)能和資源平衡的問題,為離散制造業(yè)各種資源的高速流通帶來了極大的便利。在預(yù)測中使用合理的數(shù)學(xué)模型來預(yù)測零件產(chǎn)量能給企業(yè)的生產(chǎn)計劃帶來極大的幫助,但是企業(yè)的生產(chǎn)預(yù)測卻具有復(fù)雜、多層次這些特點,這些特點給建模帶來了層層困難。本文的研究主體是基于寧夏吳忠儀表責(zé)任有限公司(簡稱吳忠儀表)的,該企業(yè)是一家離散型的閥門制造企業(yè),該企業(yè)生產(chǎn)的產(chǎn)品的特點是品種多樣、批量少、批次繁多。所以在滿足客戶需求的情況下實現(xiàn)企業(yè)資源合理的利用,就必須采取更加有效的生產(chǎn)組織方式。為了完善企業(yè)自身已有的預(yù)測系統(tǒng),提高預(yù)測能力,企業(yè)需要建立一套新的預(yù)測模式。通過分析企業(yè)的歷史生產(chǎn)數(shù)據(jù),并對數(shù)據(jù)進行清洗然后根據(jù)數(shù)據(jù)的特點選擇合適的算法來構(gòu)建預(yù)測模型。在算法上本論文提出了一種用遺傳算法優(yōu)化的支持向量回歸機來對數(shù)據(jù)進行預(yù)測,在預(yù)測模式上提出了兩種預(yù)測模式,通過算法和模式的組合來更加準(zhǔn)確的對企業(yè)的通用零件的產(chǎn)量進行預(yù)測。然后使用MATLAB對構(gòu)建的預(yù)測模型進行仿真模擬驗證模型的準(zhǔn)確性,最后通過編程實現(xiàn)了預(yù)測系統(tǒng),有效的實現(xiàn)了對企業(yè)通用零件的預(yù)測。企業(yè)通用零件預(yù)測系統(tǒng)的實現(xiàn)具有以下兩方面的意義:第一,提高了對訂單的處理能力和響應(yīng)能力;第二,提高了該企業(yè)的市場競爭力。
[Abstract]:In order to realize the dynamic management of enterprise information, enterprises usually adopt the method of Advanced planning and scheduling. This is a method that can take into account the important factors in production such as time, order, inventory, and forecast output, and obtain all kinds of dynamic changes at any time in the production process of the enterprise to adjust the production to meet the market changes. It solves the problem of enterprise capacity and resource balance, and brings great convenience to the rapid circulation of various resources in discrete manufacturing industry. The use of reasonable mathematical model to predict the production of parts can bring great help to the production plan of the enterprise, but the production forecast of the enterprise has the characteristics of complex and multi-level, which brings many difficulties to the modeling. The main body of this paper is based on Ningxia Wu Zhong instrument liability Co., Ltd. (Wu Zhong instrument), the enterprise is a discrete valve manufacturing enterprise, the characteristics of the products produced by the enterprise is a variety of products, fewer batches and lots of batches. Therefore, it is necessary to adopt more effective production organization mode to realize the rational utilization of enterprise resources under the condition of satisfying customer demand. In order to perfect the existing forecasting system and improve the forecasting ability, enterprises need to establish a new forecasting model. By analyzing the historical production data of the enterprise and cleaning the data, the prediction model is constructed by selecting the appropriate algorithm according to the characteristics of the data. In this paper, a support vector regression machine optimized by genetic algorithm is proposed to predict the data, and two prediction models are proposed in this paper. Through the combination of algorithms and patterns to more accurately predict the production of common parts of the enterprise. Then MATLAB is used to simulate and verify the veracity of the model. Finally, the prediction system is realized by programming, which effectively realizes the prediction of the common parts of the enterprise. The realization of the general part prediction system has the following two meanings: firstly, the ability to deal with and respond to the orders is improved; secondly, the market competitiveness of the enterprise is improved.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F426.46;TP311.52

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