畢業(yè)論文外文翻譯-供應(yīng)鏈管理環(huán)境下的庫存優(yōu)化

1、供應(yīng)鏈管理環(huán)境下的庫存優(yōu)化 摘要：傳統(tǒng)供應(yīng)鏈模式下的庫存優(yōu)化由于缺乏必要的信息，在模型的求解過程中難以得到符合實(shí)際要求的最優(yōu)解。本文分析了傳統(tǒng)企業(yè)庫存優(yōu)化與供應(yīng)鏈管理環(huán)境下庫存優(yōu)化的運(yùn)作機(jī)理，提出在供應(yīng)鏈管理環(huán)境下可以借助多層BP神經(jīng)網(wǎng)絡(luò)改進(jìn)傳統(tǒng)庫存模型，以得到更為滿意地最優(yōu)庫存策略。并依據(jù)某一鋼材現(xiàn)貨公司的庫存情況給出具體的應(yīng)用。關(guān)鍵詞：供應(yīng)鏈 供應(yīng)鏈管理 庫存 BP神經(jīng)網(wǎng)絡(luò) 優(yōu)化1.引言供應(yīng)鏈管理（簡稱SCM）是當(dāng)今的一個(gè)熱門話題。這個(gè)詞來自關(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管理整個(gè)信息流，材料和來自于原材料供應(yīng)商的服務(wù)通過工廠和倉庫直

2、到最終的客戶。成功的供應(yīng)鏈管理需要有一個(gè)一體化的系列活動(dòng)納入一個(gè)緊密無間的過程。但是，在供應(yīng)鏈的每一個(gè)環(huán)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存。相反，對企業(yè)來說存貨實(shí)際上是一種浪費(fèi)。國內(nèi)外專家在庫存優(yōu)化領(lǐng)域已取得了很大的研究，做了許多庫存優(yōu)化的模型。所有這些模式在供應(yīng)鏈管理的思想應(yīng)運(yùn)而生之前已經(jīng)取得了，但這些模型沒有考慮上游和下游企業(yè)。這些作為稀缺的信息優(yōu)化模型僅僅利用概率模型來適應(yīng)信息統(tǒng)計(jì)基礎(chǔ)上需求的變化。通常情況下，通過這種方式制作的模型因?yàn)樘^復(fù)雜而很難操作。另一方面，影響存貨清單的各因素之間的關(guān)系是非線性的，因此很難作出一個(gè)定量和明確的數(shù)學(xué)關(guān)系，而且這些最佳的成果也不能

3、滿足實(shí)際應(yīng)用。人工神經(jīng)網(wǎng)絡(luò)本身的自我學(xué)習(xí)和多映射的能力，可以探索復(fù)雜系統(tǒng)，使復(fù)雜的模型簡單化。在人工神經(jīng)網(wǎng)絡(luò)里，隱藏在網(wǎng)絡(luò)中的信息所作的聯(lián)系的神經(jīng)元，它可以處理多種定量關(guān)系。即神經(jīng)網(wǎng)絡(luò)是一個(gè)大規(guī)模并行計(jì)算模型，它的特點(diǎn)： 很大程度的魯棒性和容錯(cuò)性； 隨時(shí)準(zhǔn)備處理與一般非線性系統(tǒng)的相關(guān)問題； 生物物理影響。因此，對于非線性問題，人工神經(jīng)網(wǎng)絡(luò)是一個(gè)很好的分析工具。本文將提出在多層次的BP神經(jīng)網(wǎng)絡(luò)的幫助下，來改進(jìn)傳統(tǒng)的庫存模型以獲得更令人滿意的優(yōu)化庫存。2.傳統(tǒng)庫存優(yōu)化模型的局限性上游和下游企業(yè)形成之前的戰(zhàn)略聯(lián)盟關(guān)系，只有一個(gè)單一的物質(zhì)流。運(yùn)行機(jī)制如下圖所示：基于傳統(tǒng)供應(yīng)鏈的運(yùn)行機(jī)制(如圖所示) ，

4、由于缺乏必要的信息，庫存決策優(yōu)化模型必須利用概率模型來適應(yīng)信息統(tǒng)計(jì)基礎(chǔ)上需求的變化。現(xiàn)在，我們給一個(gè)簡單的單周期隨機(jī)庫存模型：在這個(gè)模型中： ET(y)：價(jià)值期望的總費(fèi)用清單； c：每種產(chǎn)品制造（或購買）的費(fèi)用；h：每個(gè)產(chǎn)品的庫存成本；p：缺少每個(gè)產(chǎn)品的懲罰成本；x:開放的股票；y：該股在開放時(shí)所得；：在這個(gè)時(shí)期，它為隨機(jī)變量；（）:概率密度函數(shù)。為了盡量減少價(jià)值期望的總費(fèi)用清單的價(jià)值，即：使價(jià)值期望的總費(fèi)用清單最小，必須使。通過推導(dǎo)制定的方法得到參數(shù)的論點(diǎn)，我們將得到：；如果提供每種產(chǎn)品制造（或購買）的費(fèi)用，每個(gè)產(chǎn)品的庫存成本，缺少每個(gè)產(chǎn)品的懲罰成本的價(jià)值，我們能獲得該股在開放時(shí)所得的最佳的

5、價(jià)值股票，還可以得到在這個(gè)時(shí)代的最佳的庫存策略。正如上面提到的，這種傳統(tǒng)模式下取得的資料不足，涉及到許多相關(guān)的應(yīng)用范圍，所以這是必不可少的前提假設(shè)，因此這種模式是難以符合實(shí)際應(yīng)用的?，F(xiàn)在的主要問題集中在隨機(jī)變量的概率密度函數(shù)中。從上述分析我們知道影響隨機(jī)變量的因素是多變量非線性關(guān)系；如：產(chǎn)品的價(jià)格，銷售季節(jié)的變化，內(nèi)部收益率的總和。當(dāng)然，對于一個(gè)特定的企業(yè)、影響因素可能是可變的。因此，在這個(gè)時(shí)期的隨機(jī)變量可能不符合一個(gè)確定的概率分布，以及以這種方式獲得庫存的最優(yōu)戰(zhàn)略可能不符合現(xiàn)實(shí)的要求。3.基于機(jī)械供應(yīng)鏈管理上的模型改進(jìn)直接和深遠(yuǎn)影響到企業(yè)的供應(yīng)鏈變化的思考的決策模式: 改變傳統(tǒng)模式，阻止縱向

6、思考模式進(jìn)入橫向，縱向的思考模式打開。隨著IT和物流技術(shù)的發(fā)展，基于內(nèi)聯(lián)網(wǎng)聯(lián)網(wǎng)，互聯(lián)網(wǎng)和電子數(shù)據(jù)交換技術(shù)，企業(yè)可能有能力實(shí)現(xiàn)翻譯。在供應(yīng)鏈管理的基礎(chǔ)上機(jī)械業(yè)務(wù)的企業(yè)如下所示：根據(jù)上圖中，屬于一個(gè)具體供應(yīng)鏈的企業(yè)可以分享一些重要的信息，這些信息在傳統(tǒng)供應(yīng)鏈下是每個(gè)企業(yè)的商業(yè)秘密。有了這一信息的企業(yè)可以提高庫存的預(yù)測精度，銷售等。3.1多層BP神經(jīng)網(wǎng)絡(luò)（1）BP神經(jīng)網(wǎng)絡(luò)的摘要概括人工神經(jīng)網(wǎng)絡(luò)，人工神經(jīng)網(wǎng)絡(luò)是一種信息處理模式啟發(fā)，通過密集的相互聯(lián)系，哺乳動(dòng)物大腦處理信息的平行結(jié)構(gòu)。換言之，人工神經(jīng)網(wǎng)絡(luò)的集合的數(shù)學(xué)模型，模擬的一些觀測特性的生物神經(jīng)系統(tǒng)，并利用類比的自適應(yīng)生物學(xué)習(xí)。神經(jīng)網(wǎng)絡(luò)模式的關(guān)鍵因

7、素是新型結(jié)構(gòu)的信息處理系統(tǒng)。它是由大量的高度聯(lián)結(jié)處理單元，類似于捆綁在一起，以加權(quán)聯(lián)系，類似于突觸。這一模式的優(yōu)勢尋找一個(gè)合適的預(yù)測模型庫存清單。有眾多不同類型的人工神經(jīng)網(wǎng)絡(luò)和BP神經(jīng)網(wǎng)絡(luò)，這是進(jìn)行了反向誤差算法的訓(xùn)練。根據(jù)簡單的結(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)。（2）基本的多層BP神經(jīng)網(wǎng)絡(luò)通常BP神經(jīng)網(wǎng)絡(luò)的層次是有組織的。層是由若干包含一個(gè)激活功能的相互關(guān)聯(lián)的節(jié)點(diǎn)組成。模式通過“輸入層”提交給網(wǎng)絡(luò)，“輸入層”通過一個(gè)系統(tǒng)連接的加權(quán)對一個(gè)或更多的隱藏層進(jìn)行實(shí)際加工。隱藏層然后鏈接到一個(gè)輸出層，在那里輸出所顯示的圖形如下：BP神經(jīng)網(wǎng)絡(luò)的其他兩個(gè)要素是傳播fi，gi功能和神經(jīng)元

8、之間的互連權(quán)重，即重：Wij，sij，和閾值的價(jià)值：i，i。這些元素之間的關(guān)系程度由方程式如下:BP神經(jīng)網(wǎng)絡(luò)包含一些通過輸入模式來修改權(quán)的連接的某種形式的學(xué)習(xí)規(guī)則。雖然有許多不同類型的學(xué)習(xí)規(guī)則，但三角洲規(guī)則是BP神經(jīng)網(wǎng)絡(luò)用的最常見的學(xué)習(xí)規(guī)則。在三角洲規(guī)則里， 學(xué)習(xí)是出現(xiàn)在每個(gè)周期或時(shí)代的通過產(chǎn)出流動(dòng)激活以及重量調(diào)整誤差，向后傳播的一個(gè)監(jiān)督的過程。3.2在隨機(jī)變量的基礎(chǔ)上制作BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型作為庫存優(yōu)化模型、關(guān)鍵元素是適應(yīng)隨機(jī)變量需求的變化。 同時(shí)，影響需求的因素是可變的，在這個(gè)意義上說，他也是模型中最艱難的過程。另一方面，因?yàn)檫@些企業(yè)雙贏的關(guān)系，屬于一個(gè)具體供應(yīng)鏈的企業(yè)可以分享一些重要的信

9、息。如:操作計(jì)劃,營銷情報(bào)等信息。這些因素是非線性，為了使庫存優(yōu)化相當(dāng)精確，我們可以利用三重層BP神經(jīng)網(wǎng)絡(luò)預(yù)測的變化著的預(yù)測模型。制作BP神經(jīng)網(wǎng)絡(luò)庫存預(yù)測的關(guān)鍵部件是因素和量化的選擇。隨即變量首先要求選擇的因素必須符合在隨機(jī)變量的基礎(chǔ)上制作BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型，然后，我們也將考慮到定量的可行性?，F(xiàn)在我們根據(jù)鋼鐵企業(yè)實(shí)際的庫存條件給出一個(gè)具體的三重層BP神經(jīng)網(wǎng)絡(luò)模型來預(yù)測鋼板的需求。鋼鐵公司也是供應(yīng)鏈的一個(gè)鏈接點(diǎn)，因此它可以從它的戰(zhàn)略合作者得到一些具體信息?，F(xiàn)在，我們選擇以往時(shí)代的需求：x1，這個(gè)時(shí)期的價(jià)格：x2，內(nèi)部收益率的鋼鐵行業(yè)的總數(shù)：x3，季節(jié)因素的變化：x4，四個(gè)因素的輸入層；決策速度

10、的需求v，替代的隨機(jī)變量，作為輸出層；同時(shí)，隱層神經(jīng)元的數(shù)目應(yīng)取決于我們所使用的優(yōu)化方法。該模型的結(jié)構(gòu)如下所示： 在一些采樣數(shù)據(jù)里，我們可以選擇一個(gè)合適的傳遞函數(shù)并且培訓(xùn)這種模式。在這個(gè)培訓(xùn)過程中，我們可以利用矩陣實(shí)驗(yàn)室提供的神經(jīng)網(wǎng)絡(luò)工具。一旦模型訓(xùn)練達(dá)到令人滿意的水平，我們可以利用它來預(yù)測本公司的庫存需求的變化。4.結(jié)論根據(jù)上述分析，很難適當(dāng)描述傳統(tǒng)做法下影響庫存需求的因素之間的關(guān)系。另外我們知道，神經(jīng)網(wǎng)絡(luò)善于解決一些沒有解決辦法或其中一個(gè)解決方案的算法太復(fù)雜而無法找到的問題?？傮w來看，這個(gè)神經(jīng)網(wǎng)絡(luò)模型，特別是在目前用BP神經(jīng)網(wǎng)絡(luò)模型來預(yù)測庫存需求的變化的時(shí)候，是一種合適的方法。INVENT

11、ORY OPTIMUM BASED ON SUPPLY CHAIN MANAGEMENT YUN Jun YAN Bing ZHAO Yuwei College of Management of Wuhan University of Technology Hubei WuhanAbstract: Because the optimized inventory in traditional supply chain model has poor information, it becomes more difficult to obtain optimal solution complying

12、 with the practical requirements during finding, solutions to supply chain patterns. This article is intended to analyze the operational mechanism of optimized inventory in both traditional enterprises and supply chain management. Also，this article put forward to improve traditional inventory patter

13、ns with the aid of multiple-layer BP neural network so as to acquire much more satisfactory optimum tactics of inventory. This article, meanwhile, engaged in an application in accordance with specific conditions of a certain steels available company.Key Words: Supply Chain, SCM, Inventory, BP Neural

14、 Network, Optimized1.INTRODUCTIONSupply chain Management (SCM for short) is a hot topic today. The term supply chain comes from a picture of how organizations are linked together as viewed from a particular company. The idea of SCM is to apply a total systems approach to managing the entire flow of

15、information, materials, and services from raw materials suppliers through factories and warehouses to the end customers . Successful SCM requires an integration of series activities into a seamless process. However, there must be some delay and some indeterminateness in the each link of the Supply C

16、hain, so it is necessary to maintain a necessary level of inventory. To the contrary, the inventory, as to enterprises, is actually a waste. Home and abroad experts have made much study in the field of Inventory Optimum, and have made many Inventory Optimum Models. But all of these models had been m

17、ade before the thinking of SCM came into being, and these models didnt take the intercommunication of information these optimum models only utilized probabilistic models to fit the changes of requirements based on the information of statistics. Generally the modes made in this way may be too complic

18、ate to operate. On the other hand, the relationship between the factors which affect the inventory is nonlinear, so it is difficult to make a quantitative and definite mathematical relationship, also the optimum results cannot meet the applications in the real-world. Artificial neural network have t

19、he ability to learn by itself and multi-mapping, and it can explore complicate system escaping to make complicate models. In the artificial neural network models, the information hides in the network made by linked-neuron, and it can deal with multiple quantitative relationships. Namely, the ANN is

20、a massively parallel computational model, and it has characterizes : Great degree of robustness and fault tolerance; Ready to deal with problems associated with general nonlinear systems; Biophysical implications.So ANN is a good analysis tool for nonlinear problem. This paper will put forward to im

21、prove traditional inventory models with the aid of multi- layer BP neural network so as to acquire much more satisfactory optimum tactics of inventory.2.THE LIMITATION TRADITIONAL INVENTORY OPTIMUM MODELBefore the strategic alliance relationship among the upstream and downstream enterprises comes in

22、to being, there is only a single material flow. The operational mechanics is shown below:Under the operational mechanies of traditional supply chain(as show in figurel)，making inventory optimurn models; because of the lack of the necessary information, have to utilize probabilistic models to fit the

23、 changes of requirements based on the information of statistics. Now we give a simple single period random inventory model: In this model:ET (y) :The value of expectation of the total cost of inventory; c :The manufacture(or purchase)cost of per product; h: The inventory cost of per product; p :The

24、punishment cost for shorts of per product; x: The opening stock; y :The stock obtained at opening; : The demand during this epoch, it is a random variable; ():The probability density function of .In order to minimize the value of ET(y),namely，this must have.Following the method of derivation formula

25、tion which obtains parameter argument, we will get ;If give the value of c , h , p ,we can get the optimum value of stock y ;also we can get the optimum inventory tactics during this epoch.As referred above, this traditional model is made under the insufficient information, so it is essential to lea

26、d many premise hypotheses, delimitate the application range, so this kind of model is difficult to accord with the application in the real-world. The main problem focus on the probability density functions of .From the analysis above we know the factors which affect the random variable are a multi-v

27、ariable nonlinear relationship; such as: the price of product, the change of marketing seasons, the internal rate of return of total vocation. Of cause, as for a specific enterprise ,the factors may be variable may not conform to a deterministic probability distribution, and the optimum inventory st

28、rategy obtained in this way may not meet with the realistic requirement.3.MODEL IMPROVEMENT BASED ON THE MECHANIC OF SUPPLY CHAIN MANAGEMENTThe direct and profound effect to the enterprise by the think of SCM is the change of decision mode: Change from the traditional, blocked longitudinal think mod

29、e into transversal, opening think mode. With the developing of IT and logistics technology, enterprise may have the ability to realize the translation based the IntranetExtranet, Internet, and EDI technology .The operational mechanic of enterprise based on the SCM is shown below:According to the fig

30、ure above, enterprises to one specific SC may share some important information which is the business secret for enterprise under the information enterprise traditional SC. With this information enterprise can improve prediction for inventory, marketing, the precision of etc.3.1 MULTIPLE-LAYER BP NEU

31、RAL NETWORK(1) Generalization of BPNNArtificial Neural Network, ANN for short, is an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the mammalian brain processes information. In other words, artificial neural networks are collections of mathemat

32、ical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning. The key element of the ANN paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected

33、 processing elements that are analogous to ncurons and are together with weighted connections that are analogous to synapses.This model is advantage to search a suitable forecasting model for inventory.There are multitudes of different types of ANNs, and BP Neural Network is a multi-layers back prop

34、agation neural network, which is trained with the backpropagation of error algorithm. According to the simple structure and the considerable application, BPNN is the most popular ANN at the present.(2)The basics of multi-layers BPNNBPNN is typically organized in layers. Layers are made up of a numbe

35、r of interconnected nodes which contain an activation function. Patterns are presented to the network via the input layer, which communicates to one or more hidden layers where the actual processing is done via a system of weighted connections. The hidden layers then link to an output layer where th

36、e answer is output as shown in the graphic below:The other two elements of BPNN are the propagation funtions of fi ,gi and the interconnection weights between the namely the weights :wij, sij，and the value of threshold: i,i .The relationships of these elements are determinated by the equations as fo

37、llow:BPNN contains form of learning rule which modifies weights of the connections according 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, learning is a supervised process that occurs with each

38、cycle or epoch through a forward activation flow of outputs, and the backwards error propagation of weight adjustments.3.2 Making prediction BPNN model based on the random variable As to the inventory optimum model, the key element is to fitting the change of the random variable of demand , at the s

39、ame 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 win-win relationships among these enterprises. The informati

40、on 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 triple-layers BPNN to predict the variable .The key component for making inventory prediction BPNN is the choice of influ

41、ence factors and the quantification of them. First of all the criteria for the choice of factors must lie on the contribution rate for ，then we will also take account of the feasibility of quantification. Now we will give a specific triple-layers BPNN model based on the actual inventory condition of

42、 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

43、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 ，as output layer; At the same time, the number of the hidden layer neuron should depend on the optimization method which we use. The model stru

44、cture 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 corporations invent

45、ory demand. To do this, we can get the next demand based on the current data.4.CONCLUSIONAccording 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 s

46、olving problems that do not have an algorithmic solution or for which an algorithmic solution is too complex 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五分鐘搞定5000字畢業(yè)論文外文翻譯，你想要的工具都在這里!在科研過程中閱讀翻譯

47、外文文獻(xiàn)是一個(gè)非常重要的環(huán)節(jié)，許多領(lǐng)域高水平的文獻(xiàn)都是外文文獻(xiàn)，借鑒一些外文文獻(xiàn)翻譯的經(jīng)驗(yàn)是非常必要的。由于特殊原因我翻譯外文文獻(xiàn)的機(jī)會(huì)比較多，慢慢地就發(fā)現(xiàn)了外文文獻(xiàn)翻譯過程中的三大利器：Google“翻譯”頻道、金山詞霸（完整版本）和CNKI“翻譯助手。具體操作過程如下： 1.先打開金山詞霸自動(dòng)取詞功能，然后閱讀文獻(xiàn)； 2.遇到無法理解的長句時(shí)，可以交給Google處理，處理后的結(jié)果猛一看，不堪入目，可是經(jīng)過大腦的再處理后句子的意思基本就明了了； 3.如果通過Google仍然無法理解，感覺就是不同，那肯定是對其中某個(gè)“常用單詞”理解有誤，因?yàn)槟承﹩卧~看似很簡單，但是在文獻(xiàn)中有特殊的意思，這時(shí)就可以通過CNKI的“翻譯助手”來查詢相關(guān)單詞的意思，由于CNKI的單詞意思都是來源與大量的文獻(xiàn)，所以它的吻合率很高。 另外，在翻譯過程中最好以“段落”或者“長句”作為翻譯的基本單位，這樣才不會(huì)造成“只見樹木，不見森林”的誤導(dǎo)。四大工具： 1、Google翻譯：http:/www.goo