Yu-Qiang Feng

Integrated Multi-Agent-Based System for Agile Supply Chain Management

Nowadays agile supply chain management has got much attention and become a new competitive methodology. One of its most important characteristics is the ability to reconfigure dynamically and quickly according to demand changes in the market. In this paper, concepts and characteristics of agile supply chain are discussed and then an integrated system for agile supply chain management is presented based on multi-agent theory, in which the supply chain is managed by a set of intelligent agents for one or more activities. Agent functionalities and responsibilities are defined respectively. It is necessary that it has potential for practical application

Dynamic pricing: ecommerce - oriented price setting algorithm

Pricing in electronic commerce is based on bargaining. Pricing models that can fast change prices during transaction on the consequence of the buyers needs is beneficial to electronic commerce. Demand sensitive model is one of the pricing models that can be used for fast changes of prices in electronic commerce. Price setting algorithm for demand sensitive model helps sellers to get decision variables, price per unit that maximizes profit for the quantity ordered by buyers. In this paper we analyze the price setting algorithms of demand sensitive model. We use a simple example to explain how the changes of price elasticity of demand changes price per unit, gross margin and quantity demanded. Also we show how the changes of quantity demanded changes the unit price and marginal cost. The investigation shows that an increase in demand ordered decreases price per unit of a good, at the same time increasing profit margin to seller and decreasing production cost. The seller extracts some of the buyers surplus value as profits with residual surplus remaining with the buyer over and above the actual price paid. Buyers do not pay the same amount of total price for the good ordered within the same group of order, because of the difference in the net browsing cost.

An Improved Document Classification Approach with Maximum Entropy and Entropy Feature Selection

Document classification is an important task in the field of document management. Bayesian model needs the feature independent assumption; artificial neural network suffers from the overfitting problem; support vector machine (SVM) does not do well in real-value feature. This paper proposes to combine entropy and machine learning techniques for document classification. Firstly, the cross entropy and average mutual information are presented to effectively extract the features. Secondly, the support vector machine and maximum entropy model is adopted respectively to perform the classification task in the feature space. Furthermore, an improved feature description instead the binary feature with the real-value is presented in this text, since the prior knowledge of each word is helpful to document classification. Finally, we compare our method with the traditional methods, and the experiment showed our method increased 2.78 % F-measures than basic ME model, and 0.95% than naive Bayes model which was smoothed by Good-Turing algorithm.

An Improved Economic Early Warning Based on Rough Set and Support Vector Machine

Economic early warning (EEW) helps decision-making by judging the tendency of economic development. However, little research is considered about the noise problem commonly existing in the economic data. Traditional EEW method such as Bayesian model needs the feature independent assumption; artificial neural network suffers from the over-fitting problem. This paper proposes a new method of combining rough sets and support vector machine, where rough set is applied to overcome the noise problem and eliminate the redundant economic information; and support vector machine based on structural risk minimization principle is used to solve the over-fitting and small-scale sample problem. The experiment indicates that our method has achieved a satisfying performance: 87.5% in precision in binary EEW, which is a desirable precision in EEW

E-Commerce Oriented Automated Negotiation Based on FIPA Interaction Protocol Specification

Automated negotiation system with self-interested agent is becoming increasingly important in E-Commerce. If agents are to negotiate automatically with one another, they must share a formal protocol, specifying possible actions. In this paper, we firstly give a survey of some negotiation models, and then briefly describe the foundation for intelligent physical agents (FIPA) interaction protocol specification. Then a bargain protocol for E-Commerce oriented automated negotiation is presented, that can be seen as an extension of FIPA interaction specifications. This protocol, with formal and intuitive semantics, can be used in automated negotiation among agents coming from different organizations if they follow the specification.

Applying case-based reasoning to multi-attribute e-purchasing decision

This paper proposes a multi-round e-purchasing mode. The theory of procurement decision traditionally assumes that the offered quantity and quality is fixed prior to source selection. Multi-attribute procurement allows negotiation over price and other quantitative or qualitative attributes such as color, weight, or delivery time. It promises higher market efficiency that more effective information of buyers preferences and suppliers offerings exchanges. Its focused that applying case-based reasoning from offers to winner determination in multi-attribute e-purchasing decision. Our contribution is fourfold: first, the winner determination problem is analyzed in case of multiple sourcing include new unfamiliar suppliers; second, the concept of multi-attribute procurement is extended to allow for configurable offers by suppliers following buyers preference; third, buyer and suppliers may on-line learn preferences from each other in the e-purchasing process through case-based reasoning. Fourth, the models and decision algorithm are provided and illustrated with an example. These extensions provide buyer with more flexibility in the specification of their procurement request and allow for an efficient information exchange among participants, and finally, the buyer can enhance his profit.

Application of artificial immune algorithm in e-commerce oriented negotiation support system

Negotiation support system has become increasingly important since the advent of electronic commerce. But the existing NSS cannot conveniently, timely and effectively help negotiators make decisions in the real-time bargaining. Since immune algorithms have the advantages of converging global search space fast, intrinsic parallelism and resolving the optimization problems with simulating the immune system, in this paper we attempt to apply artificial immune algorithm to providing solution supporting in the electronic commerce oriented NSS in order to offer optimal or near-optimal solutions for negotiators. We verify the feasibility and special superiority of this method through theory analysis and experiments.

Modeling Negotiating Agent for Automated Negotiation

Traditional research in automated negotiation is focused on negotiation protocol and strategy, but they cannot satisfy all the requirements for realizing a practical automated negotiation system. This paper studies automated negotiation from a new point, focuses on agents independent decision-making process, not on traditional negotiations decision-making model; proposes a novel concept, namely negotiating agent, argues its significance in construction of automated negotiation system; in order to explain the concept clearly, formally defines automated negotiations abstract concept model, which is made up of three sub-concept models, they are negotiation environment, negotiation process and negotiating agent; finally, on the basis of concept model, designs negotiating agents architecture, which can support both goal-directed reasoning and reactive response.

Negotiating agent: concept, architecture and communication model

Traditional research in automated negotiation focuses on negotiation protocol and strategy. This paper studies automated negotiation from a new point, proposes a novel concept, namely negotiating agent, argues its significance in construction of automated negotiation system; designs its architecture, which can support both goal-directed reasoning and reactive response. In order to construct an interaction mechanism among negotiating agents, a communication model is proposed, in which the negotiation language used by agents is defined. Design of the communication model and the language has been attempted in such a way so as to provide general support for a wide variety of commercial negotiation circumstances, and therefore to be particularly suitable for electronic commerce. Finally, the design and expression of the negotiation ontology are discussed.

Hopfield neural network for the optimal cost

This paper investigates the energy function of Hopfield neural network. The Hopfield energy function converges to the minimal. It is proposed for the optimal cost of production functions which provide the marginal cost for the unit price of the product. The two simple examples of production functions are used for Hopfield energy convergence. The examples confirm the convergence of production costs within their isoquants. The production costs continue to decrease up to the point where they cannot decrease any more. Those are the points of the optimal costs, from which we obtain the marginal cost to be inserted automatic into the formula for the calculation of the selling price of the product.