外文翻譯---供應(yīng)鏈管理環(huán)境下的庫(kù)存優(yōu)化-環(huán)境工程-資料下載頁(yè)
【導(dǎo)讀】到符合實(shí)際要求的最優(yōu)解。本文分析了傳統(tǒng)企業(yè)庫(kù)存優(yōu)化與供應(yīng)鏈管理環(huán)境下庫(kù)存優(yōu)化的。到更為滿意地最優(yōu)庫(kù)存策略。并依據(jù)某一鋼材現(xiàn)貨公司的庫(kù)存情況給出具體的應(yīng)用。供應(yīng)鏈管理是當(dāng)今的一個(gè)熱門(mén)話題。這個(gè)詞來(lái)自關(guān)于作為一個(gè)特定的公。司是如何組織聯(lián)系在一起的一幅圖片。供應(yīng)鏈管理的想法是采用整體的方法來(lái)管理整個(gè)信。息流，材料和來(lái)自于原材料供應(yīng)商的服務(wù)通過(guò)工廠和倉(cāng)庫(kù)直到最終的客戶。管理需要有一個(gè)一體化的系列活動(dòng)納入一個(gè)緊密無(wú)間的過(guò)程。節(jié)必然有一些延誤和一些不確定性，因此必須保持必要的庫(kù)存。相反，對(duì)企業(yè)來(lái)說(shuō)存貨實(shí)。際上是一種浪費(fèi)。系，而且這些最佳的成果也不能滿足實(shí)際應(yīng)用。隨時(shí)準(zhǔn)備處理與一般非線性系統(tǒng)的相關(guān)問(wèn)題；BP神經(jīng)網(wǎng)絡(luò)的幫助下，來(lái)改進(jìn)傳統(tǒng)的庫(kù)存模型以獲得更令人滿意的優(yōu)化庫(kù)存。根據(jù)簡(jiǎn)單的結(jié)構(gòu)和大量的應(yīng)用，人工神經(jīng)網(wǎng)絡(luò)是目前最流行的神經(jīng)網(wǎng)絡(luò)。制作BP神經(jīng)網(wǎng)絡(luò)庫(kù)存預(yù)測(cè)的關(guān)鍵部件是因素和量化的選擇。
【正文】 to the input patterns. Although there are many different kinds of learning rules, what BPNN uses the most often is the delta rule. With the delta rule, 39。learning39。 is a supervised process that occurs with each cycle or 39。epoch39。 through a forward activation flow of outputs, and the backwards error propagation of weight adjustments. Making prediction BPNN model based on the random variable ξ As to the inventory optimum model, the key element is to fitting the change of the random variable of demand ξ, at the same time, the factors which affect the demand are variable, in this sense, it is also the most difficult process in the model. On the other hand enterprises which belonged to one specific SC can share some important information, due to the winwin relationships among these enterprises. The information such as: the operational plan, the marketing intelligence etc. These factors are nonlinear, in order to obtain a considerable precision for inventory optimum, we can utilize a triplelayers BPNN to predict the variable ξ. The key ponent for making inventory prediction BPNN is the choice of influence factors and the quantification of them. First of all the criteria for the choice of factors must lie on the contribution rate for ξ， then we will also take account of the feasibility of quantification. Now we will give a specific triplelayers BPNN model based on the actual inventory condition of a steel corporation to predict the change of the demand of the steel plate. This steel corporation is also a link in a supply chain, so it can get some specific information from its strategic cooperators. Now we select the demand of previous epoch: x1，the price of this epoch:x2， the internal rate of return of total steel vocation: x3， the factor of season change: x 4,making the four factors as input layer。 making the demand velocity v,the substitution of ξ， as output layer。 At the same time, the number of the 39。hidden layer39。 neuron should depend on the optimization method which we use. The model structure is show below: With some sampled data, we can select a suitable transfer function and train this model. In the process of training, we can use the ANN tools provided by MATLAB. Once the model is trained to a satisfactory level, we can utilize it to predict the change of this corporation39。s inventory demand. To do this, we can get the next demand based on the current data. According to the anaaysis above, it is difficult to describe adequately the relationship of the factors which affect the demand of inventory with conventional approaches. Also we know that the ANNs are good at solving problems that do not have an algorithmic solution or for which an algorithmic solution is too plex to be found. Summarily, The ANNs model, as to predicting the change of inventory demand, is a suitable approach at the prrsent ,especially for BPNN mod
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