1、附录一英文原文 INVENTORY OPTIMUM BASED ON SUPPLY CHAIN MANAGEMENT YUN Jun YAN Bing ZHAO Yuwei College of Management of WuhanUniversity of TechnologyHubeiWuhan Abstract: Because the optimized inventory in traditional supply chain model has poor information, it becomes more difficult to obtain optimal soluti
2、on complying 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 inv
3、entory patterns 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, Invent
4、ory, BP Neural Network, Optimized 1.INTRODUCTION Supply 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
5、 entire flow of 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
6、 of the Supply Chain, 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 m
7、odels had been made 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 ma
8、y be too complicate 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 neura
9、l network have the 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. Nam
10、ely, the ANN is 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
11、put forward to improve 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 MODEL Before the strategic alliance relationship among the upstream and downstream e
12、nterprises comes into 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 probabilist
13、ic models to fit the 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 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 .
