發(fā)布時間：2018-04-25 19:08

  本文選題：供應鏈 + 質(zhì)量成本優(yōu)化　； 參考：《福州大學》2014年碩士論文

【摘要】：質(zhì)量管理是現(xiàn)代企業(yè)生產(chǎn)經(jīng)營的關(guān)鍵點。在當前激烈的競爭市場經(jīng)濟環(huán)境中,供應鏈之間的角逐早已取代了企業(yè)之間的競爭。因此在供應鏈質(zhì)量管理的背景下,具備成本優(yōu)勢已成為供應鏈贏得競爭優(yōu)勢的重要手段。本文在供應鏈環(huán)境下研究質(zhì)量成本,既充分利用了供應鏈集成優(yōu)勢,又彌補了傳統(tǒng)質(zhì)量成本管理在企業(yè)互動和相互合作方面的缺陷。通過探討供應鏈質(zhì)量成本優(yōu)化,為企業(yè)追求低成本下的高質(zhì)量產(chǎn)品提供了新的思路,也為供應鏈經(jīng)營管理中的質(zhì)量成本優(yōu)化實踐提供了相關(guān)的理論參考。供應鏈質(zhì)量成本優(yōu)化包括供應鏈企業(yè)內(nèi)部和供應鏈企業(yè)之間的質(zhì)量成本優(yōu)化。本文選擇在單個企業(yè)質(zhì)量成本優(yōu)化的基礎(chǔ)上探討相鄰企業(yè)間的質(zhì)量成本優(yōu)化方法,因而引入供應商質(zhì)量成本來定義和解決兩階段供應鏈質(zhì)量成本優(yōu)化問題。供應商質(zhì)量成本優(yōu)化從相關(guān)定義、科目核算和優(yōu)化流程等三個方面進行詳細論述。在供應商質(zhì)量成本涵義中,首先確定以顧客滿意為標準的質(zhì)量水平,同時考慮供應鏈質(zhì)量風險傳遞性特征,引入下游企業(yè)質(zhì)量成本影響系數(shù)。隨后在質(zhì)量水平與下游企業(yè)質(zhì)量成本影響系數(shù)這兩個變量的基礎(chǔ)上構(gòu)建了供應商質(zhì)量成本核算科目表,通過采集供應商質(zhì)量成本核算科目表得到質(zhì)量成本優(yōu)化數(shù)據(jù)。為了能詳細了解質(zhì)量水平、下游企業(yè)質(zhì)量成本影響系數(shù)與總質(zhì)量成本之間的函數(shù)關(guān)系,本文采用徑向基神經(jīng)網(wǎng)絡(luò)來逼近這種關(guān)系。以供應商質(zhì)量成本核算科目表數(shù)據(jù),作為RBF神經(jīng)網(wǎng)絡(luò)的訓練數(shù)據(jù),在滿足精度要求的擬合網(wǎng)絡(luò)中通過樣本數(shù)據(jù)測試網(wǎng)絡(luò)的精度。遺傳算法善于處理傳統(tǒng)計算方法難于解決的復雜系統(tǒng)優(yōu)化問題。在基于遺傳算法的質(zhì)量成本函數(shù)優(yōu)化中,設(shè)定目標優(yōu)化質(zhì)量成本值與徑向基神經(jīng)網(wǎng)絡(luò)預測輸出值的絕對值差為遺傳算法的個體適應度函數(shù),經(jīng)過遺傳操作后,得到滿足目標質(zhì)量成本值的最優(yōu)質(zhì)量水平與下游企業(yè)質(zhì)量成本影響系數(shù)。最后,通過一個實證分析來證實基于RBF-GA的供應鏈質(zhì)量成本優(yōu)化模型的有效性,總結(jié)出供應鏈企業(yè)可以通過調(diào)節(jié)質(zhì)量水平與下游企業(yè)影響系數(shù)來確定質(zhì)量成本控制效果,為企業(yè)進行供應鏈質(zhì)量成本科目核算提供了一定的依據(jù)。經(jīng)過多次仿真說明,本文提出的非線性系統(tǒng)尋優(yōu)方法能以較快的收斂速度找到近似最優(yōu)解,結(jié)果證明徑向基神經(jīng)網(wǎng)絡(luò)和遺傳算法來尋求供應鏈質(zhì)量成本優(yōu)化的方法是可行的。
[Abstract]:Quality management is the key point of modern enterprise production and management. In the current competitive market economic environment, the competition between supply chains has already replaced the competition between enterprises. Therefore, under the background of supply chain quality management, having the cost advantage has become an important means to win the competitive advantage of the supply chain. The study of quality cost not only makes full use of the advantages of supply chain integration, but also makes up for the shortcomings of traditional quality cost management in the interaction and cooperation of enterprises. By discussing the optimization of quality cost of supply chain, it provides a new idea for enterprises to pursue high quality products under low cost, and also optimizes the quality cost in the management of supply chain. The practice provides the relevant theoretical reference. The quality cost optimization of supply chain includes the quality cost optimization between the internal and supply chain enterprises of the supply chain. This paper selects the method of quality cost optimization between adjacent enterprises on the basis of the optimization of individual enterprise quality cost, thus introducing the quality cost of suppliers to define and solve two. The quality cost optimization of stage supply chain is discussed in detail from three aspects: related definition, subject accounting and optimization process. In the meaning of supplier quality cost, the quality level of customer satisfaction is first determined, and the quality risk of supply chain is considered, and the downstream enterprise is introduced into the supply chain. On the basis of the two variables, the quality cost and the quality cost influence coefficient of the downstream enterprise, the supplier quality cost accounting subject table is constructed, and the quality cost optimization data are obtained by collecting the supplier quality cost accounting subject table. In order to understand the quality level in detail, the quality cost of the downstream enterprise is reflected. The function relationship between the RBF and the total quality cost is used in this paper. This paper uses the RBF neural network to approach this relationship. As the training data of the RBF neural network, the accuracy of the network is tested by the sample data in the fitting network satisfying the precision requirements. It is difficult to solve the problem of complex system optimization. In the optimization of the quality cost function based on genetic algorithm, the absolute value difference between the optimal quality cost value of the target and the predicted output value of the radial basis neural network is the individual fitness function of the genetic algorithm. After the genetic operation, the optimal quality is obtained to meet the quality cost value of the target. In the end, the effectiveness of the supply chain quality cost optimization model based on RBF-GA is confirmed by an empirical analysis. It is concluded that the supply chain enterprises can determine the quality cost control effect by adjusting the quality level and the influence coefficient of the downstream enterprises, so as to carry out the supply chain quality cost for the enterprises. After many simulations, the nonlinear system optimization method proposed in this paper can find the approximate optimal solution at a faster rate of convergence. The results prove that the RBF neural network and genetic algorithm are feasible to seek the quality cost optimization of the supply chain.

【學位授予單位】：福州大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F274;F275.3

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本文編號：1802577