發(fā)布時間：2018-05-15 04:18

  本文選題：煤礦裝備 + 庫存分類��； 參考：《遼寧工程技術大學》2014年碩士論文

【摘要】：隨著經(jīng)濟的發(fā)展,庫存在企業(yè)中的地位不斷提升。作為供應鏈的重要環(huán)節(jié),它發(fā)揮著平衡物流采購和生產(chǎn)消耗的重要作用。然而經(jīng)濟發(fā)展的背后企業(yè)庫存問題卻日趨尖銳,逐漸的成為阻礙企業(yè)可持續(xù)發(fā)展的主要障礙。因此,在保證產(chǎn)品質(zhì)量的前提下優(yōu)化庫存結(jié)構(gòu),降低庫存成本已成為節(jié)約整個企業(yè)成本的重要環(huán)節(jié)和保持行業(yè)競爭力的有效手段。張家口煤機公司是煤礦機械裝備制造領域的典型企業(yè),長期的庫存積壓和居高不下的庫存成本已成為阻礙企業(yè)發(fā)展的最大障礙。因此,研究該企業(yè)的庫存優(yōu)化對于該行業(yè)的可持續(xù)發(fā)展具有重要的意義。本文以張煤機公司原輔材料庫為研究對象,針對該企業(yè)庫存的實際情況,首先建立了基于ABC管理的灰色聚類評價模型。選取采購提前期、供應商數(shù)量、月需求量、客戶指定、功能性、物料的單位價值和庫存成本作為評價指標對原輔材料庫中的物料進行分類。通過綜合打分的方式,確定本文所選取的該公司的20種物料的A、B、C類別。其次,運用神經(jīng)網(wǎng)絡對建立的基于ABC管理的灰色聚類評價模型進行評價,將上文中參與評價的20種物料的前10種物料作為訓練樣本,后10種作為測試樣本,并把通過ABC管理的灰色聚類評價所得出的綜合評分作為期望值,對評價的結(jié)果進行測試,證明本文所建立的上述模型的有效性,以及通過該方式進行物資分類的合理性。最后,針對A類和B類庫存物資,進行庫存分類優(yōu)化,針對A類和B類物料建立了以訂貨量為參數(shù)的庫存模型,運用遺傳算法求解最優(yōu)訂貨量和安全庫存。
[Abstract]:With the development of economy, the status of inventory in enterprises is increasing. As an important link of supply chain, it plays an important role in balancing logistics procurement and production consumption. However, the problem of enterprise inventory is becoming more and more acute behind the economic development, which gradually becomes the main obstacle to the sustainable development of enterprises. Therefore, optimizing inventory structure and reducing inventory cost under the premise of ensuring product quality have become an important link to save the cost of the whole enterprise and an effective means to maintain the competitiveness of the industry. Zhangjiakou Coal Machine Company is a typical enterprise in the field of coal mine machinery and equipment manufacturing. The long-term inventory backlog and high inventory cost have become the biggest obstacle to the development of enterprises. Therefore, it is of great significance to study the inventory optimization of the enterprise for the sustainable development of the industry. In this paper, taking the raw and auxiliary material library of Zhangshan Coal Machine Company as the research object, according to the actual situation of the enterprise inventory, a grey clustering evaluation model based on ABC management is established. Purchasing lead time, number of suppliers, monthly demand, customer designation, functionality, unit value of materials and inventory cost are selected as evaluation indicators to classify the materials in the raw and auxiliary materials library. In this paper, 20 kinds of materials of this company are selected by the way of comprehensive scoring. Secondly, using neural network to evaluate the grey cluster evaluation model based on ABC management, the first 10 kinds of materials of the 20 kinds of materials involved in the evaluation above are used as training samples, the last 10 kinds of materials as test samples. The comprehensive score obtained by grey clustering evaluation managed by ABC is taken as the expected value and the evaluation results are tested to prove the validity of the above model established in this paper and the rationality of the material classification by this method. Finally, the inventory classification optimization is carried out for class A and B inventory materials, and the inventory model based on order quantity is established for class A and B materials. The genetic algorithm is used to solve the optimal order quantity and safety inventory.
【學位授予單位】：遼寧工程技術大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F274;F426.4

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