毕业论文外文翻译-供应链管理环境下的库存优化.doc

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1、供应链管理环境下的库存优化 摘要：传统供应链模式下的库存优化由于缺乏必要的信息，在模型的求解过程中难以得到符合实际要求的最优解。本文分析了传统企业库存优化与供应链管理环境下库存优化的运作机理，提出在供应链管理环境下可以借助多层BP神经网络改进传统库存模型，以得到更为满意地最优库存策略。并依据某一钢材现货公司的库存情况给出具体的应用。关键词：供应链 供应链管理 库存 BP神经网络 优化1.引言供应链管理（简称SCM）是当今的一个热门话题。这个词来自关于作为一个特定的公司是如何组织联系在一起的一幅图片。供应链管理的想法是采用整体的方法来管理整个信息流，材料和来自于原材料供应商的服务通过工厂和仓库直

2、到最终的客户。成功的供应链管理需要有一个一体化的系列活动纳入一个紧密无间的过程。但是，在供应链的每一个环节必然有一些延误和一些不确定性，因此必须保持必要的库存。相反，对企业来说存货实际上是一种浪费。国内外专家在库存优化领域已取得了很大的研究，做了许多库存优化的模型。所有这些模式在供应链管理的思想应运而生之前已经取得了，但这些模型没有考虑上游和下游企业。这些作为稀缺的信息优化模型仅仅利用概率模型来适应信息统计基础上需求的变化。通常情况下，通过这种方式制作的模型因为太过复杂而很难操作。另一方面，影响存货清单的各因素之间的关系是非线性的，因此很难作出一个定量和明确的数学关系，而且这些最佳的成果也不能

3、满足实际应用。人工神经网络本身的自我学习和多映射的能力，可以探索复杂系统，使复杂的模型简单化。在人工神经网络里，隐藏在网络中的信息所作的联系的神经元，它可以处理多种定量关系。即神经网络是一个大规模并行计算模型，它的特点： 很大程度的鲁棒性和容错性； 随时准备处理与一般非线性系统的相关问题； 生物物理影响。因此，对于非线性问题，人工神经网络是一个很好的分析工具。本文将提出在多层次的BP神经网络的帮助下，来改进传统的库存模型以获得更令人满意的优化库存。2.传统库存优化模型的局限性上游和下游企业形成之前的战略联盟关系，只有一个单一的物质流。运行机制如下图所示：基于传统供应链的运行机制(如图所示) ，

4、由于缺乏必要的信息，库存决策优化模型必须利用概率模型来适应信息统计基础上需求的变化。现在，我们给一个简单的单周期随机库存模型：在这个模型中： ET(y)：价值期望的总费用清单； c：每种产品制造（或购买）的费用；h：每个产品的库存成本；p：缺少每个产品的惩罚成本；x:开放的股票；y：该股在开放时所得；：在这个时期，它为随机变量；（）:概率密度函数。为了尽量减少价值期望的总费用清单的价值，即：使价值期望的总费用清单最小，必须使。通过推导制定的方法得到参数的论点，我们将得到：；如果提供每种产品制造（或购买）的费用，每个产品的库存成本，缺少每个产品的惩罚成本的价值，我们能获得该股在开放时所得的最佳的

5、价值股票，还可以得到在这个时代的最佳的库存策略。正如上面提到的，这种传统模式下取得的资料不足，涉及到许多相关的应用范围，所以这是必不可少的前提假设，因此这种模式是难以符合实际应用的。现在的主要问题集中在随机变量的概率密度函数中。从上述分析我们知道影响随机变量的因素是多变量非线性关系；如：产品的价格，销售季节的变化，内部收益率的总和。当然，对于一个特定的企业、影响因素可能是可变的。因此，在这个时期的随机变量可能不符合一个确定的概率分布，以及以这种方式获得库存的最优战略可能不符合现实的要求。3.基于机械供应链管理上的模型改进直接和深远影响到企业的供应链变化的思考的决策模式: 改变传统模式，阻止纵向

6、思考模式进入横向，纵向的思考模式打开。随着IT和物流技术的发展，基于内联网联网，互联网和电子数据交换技术，企业可能有能力实现翻译。在供应链管理的基础上机械业务的企业如下所示：根据上图中，属于一个具体供应链的企业可以分享一些重要的信息，这些信息在传统供应链下是每个企业的商业秘密。有了这一信息的企业可以提高库存的预测精度，销售等。3.1多层BP神经网络（1）BP神经网络的摘要概括人工神经网络，人工神经网络是一种信息处理模式启发，通过密集的相互联系，哺乳动物大脑处理信息的平行结构。换言之，人工神经网络的集合的数学模型，模拟的一些观测特性的生物神经系统，并利用类比的自适应生物学习。神经网络模式的关键因

7、素是新型结构的信息处理系统。它是由大量的高度联结处理单元，类似于捆绑在一起，以加权联系，类似于突触。这一模式的优势寻找一个合适的预测模型库存清单。有众多不同类型的人工神经网络和BP神经网络，这是进行了反向误差算法的训练。根据简单的结构和大量的应用，人工神经网络是目前最流行的神经网络。（2）基本的多层BP神经网络通常BP神经网络的层次是有组织的。层是由若干包含一个激活功能的相互关联的节点组成。模式通过“输入层”提交给网络，“输入层”通过一个系统连接的加权对一个或更多的隐藏层进行实际加工。隐藏层然后链接到一个输出层，在那里输出所显示的图形如下：BP神经网络的其他两个要素是传播fi，gi功能和神经元

8、之间的互连权重，即重：Wij，sij，和阈值的价值：i，i。这些元素之间的关系程度由方程式如下:BP神经网络包含一些通过输入模式来修改权的连接的某种形式的学习规则。虽然有许多不同类型的学习规则，但三角洲规则是BP神经网络用的最常见的学习规则。在三角洲规则里， 学习是出现在每个周期或时代的通过产出流动激活以及重量调整误差，向后传播的一个监督的过程。3.2在随机变量的基础上制作BP神经网络预测模型作为库存优化模型、关键元素是适应随机变量需求的变化。 同时，影响需求的因素是可变的，在这个意义上说，他也是模型中最艰难的过程。另一方面，因为这些企业双赢的关系，属于一个具体供应链的企业可以分享一些重要的信

9、息。如:操作计划,营销情报等信息。这些因素是非线性，为了使库存优化相当精确，我们可以利用三重层BP神经网络预测的变化着的预测模型。制作BP神经网络库存预测的关键部件是因素和量化的选择。随即变量首先要求选择的因素必须符合在随机变量的基础上制作BP神经网络预测模型，然后，我们也将考虑到定量的可行性。现在我们根据钢铁企业实际的库存条件给出一个具体的三重层BP神经网络模型来预测钢板的需求。钢铁公司也是供应链的一个链接点，因此它可以从它的战略合作者得到一些具体信息。现在，我们选择以往时代的需求：x1，这个时期的价格：x2，内部收益率的钢铁行业的总数：x3，季节因素的变化：x4，四个因素的输入层；决策速度

10、的需求v，替代的随机变量，作为输出层；同时，隐层神经元的数目应取决于我们所使用的优化方法。该模型的结构如下所示： 在一些采样数据里，我们可以选择一个合适的传递函数并且培训这种模式。在这个培训过程中，我们可以利用矩阵实验室提供的神经网络工具。一旦模型训练达到令人满意的水平，我们可以利用它来预测本公司的库存需求的变化。4.结论根据上述分析，很难适当描述传统做法下影响库存需求的因素之间的关系。另外我们知道，神经网络善于解决一些没有解决办法或其中一个解决方案的算法太复杂而无法找到的问题。总体来看，这个神经网络模型，特别是在目前用BP神经网络模型来预测库存需求的变化的时候，是一种合适的方法。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五分钟搞定5000字毕业论文外文翻译，你想要的工具都在这里!在科研过程中阅读翻译

47、外文文献是一个非常重要的环节，许多领域高水平的文献都是外文文献，借鉴一些外文文献翻译的经验是非常必要的。由于特殊原因我翻译外文文献的机会比较多，慢慢地就发现了外文文献翻译过程中的三大利器：Google“翻译”频道、金山词霸（完整版本）和CNKI“翻译助手。具体操作过程如下： 1.先打开金山词霸自动取词功能，然后阅读文献； 2.遇到无法理解的长句时，可以交给Google处理，处理后的结果猛一看，不堪入目，可是经过大脑的再处理后句子的意思基本就明了了； 3.如果通过Google仍然无法理解，感觉就是不同，那肯定是对其中某个“常用单词”理解有误，因为某些单词看似很简单，但是在文献中有特殊的意思，这时

48、就可以通过CNKI的“翻译助手”来查询相关单词的意思，由于CNKI的单词意思都是来源与大量的文献，所以它的吻合率很高。 另外，在翻译过程中最好以“段落”或者“长句”作为翻译的基本单位，这样才不会造成“只见树木，不见森林”的误导。四大工具： 1、Google翻译： google，众所周知，谷歌里面的英文文献和资料还算是比较详实的。我利用它是这样的。一方面可以用它查询英文论文，当然这方面的帖子很多，大家可以搜索，在此不赘述。回到我自己说的翻译上来。下面给大家举个例子来说明如何用吧比如说“电磁感应透明效应”这个词汇你不知道他怎么翻译，首先你可以在CNKI里查中文的，根据它们的关键词中英文对照来做，一般比较准确。 在此主要是