No abstract

A relaxation strategy with fuzzy constraints for supplier selection in a power A relaxation strategy with fuzzy constraints for supplier selection in a power market market Abstract Abstract A power market is a special kind of e-markets. In a power market, all trading processes are related to three parties: buyers, suppli-ers and brokers. A broker acts as middlemen between buyers and sup-pliers in a trading process. In a power market, how to select a potential supplier for a buyer through a broker based on the buyer's requirements is a challenging research problem. This paper proposes relaxation strat-egy with fuzzy constraints for supplier selection. The strategy includes three components, i.e., a supplier selection, a fuzzy constraint relaxation, and a decision making. The major contributions of this paper are that (1) the trading process between buyers and suppliers through brokers is modeled by using fuzzy constraints through the consideration of multiple attributes of the buyer's requirements as well as potential power suppli-ers; and (2) a buyer can utilize a relaxation with fuzzy constraints to change its requirements in dicult situations when a broker cannot nd any supplier to satisfy a buyer's requirements. Experimental results show that our approach is successfully applied in a simulated power market.Abstract. A power market is a special kind of e-markets. In a power market, all trading processes are related to three parties: buyers, suppliers and brokers. A broker acts as middlemen between buyers and suppliers in a trading process. In a power market, how to select a potential supplier for a buyer through a broker based on the buyer's requirements is a challenging research problem. This paper proposes relaxation strategy with fuzzy constraints for supplier selection. The strategy includes three components, i.e., a supplier selection, a fuzzy constraint relaxation, and a decision making. The major contributions of this paper are that (1) the trading process between buyers and suppliers through brokers is modeled by using fuzzy constraints through the consideration of multiple attributes of the buyer's requirements as well as potential power suppliers; and (2) a buyer can utilize a relaxation with fuzzy constraints to change its requirements in difficult situations when a broker cannot find any supplier to satisfy a buyer's requirements. Experimental results show that our approach is successfully applied in a simulated power market.

A broker acts as a middleman between buyers and sellers in the trading processes to achieve its profit as well as to satisfy buyer's requirements based on seller's offers. This paper proposes a broker-based optimal matching approach in the markets. The major contributions of this paper include (1) an abstract model of a broker agent, that is applicable to a broad range of market types; (2) predicting buyers and sellers' behavior by using Bayes' rule so that a broker can identify an appropriate allocation of items between buyers and sellers; and (3) an objective function and a set of constraints to help a broker to maximize its profit under consideration of buyer and seller's total satisfaction. Experimental results demonstrate the good performance of the proposed approach in terms of satisfying buyer's requirements and maximizing broker's profit.

Abstract. Association rule mining is one of the useful techniques in data mining and knowledge discovery that extracts interesting relationships between items in datasets. Generally, the number of association rules in a particular dataset mainly depends on the measures of 'support' and 'confidence'. To choose the number of useful rules, normally, the measures of 'support' and 'confidence' need to be tried many times. In some cases, the measures of 'support' and 'confidence' are chosen by experience. Thus, it is a time consuming to find the optimal measure of 'support' and 'confidence' for the process of association rule mining in large datasets. This paper proposes a regression based approach to improve the association rule mining process through predicting the number of rules on datasets. The approach includes a regression model in a generic level for general domains and an instantiation scheme to create concrete models in particular domains for predicting the potential number of association rules on a dataset before mining. The proposed approach can be used in broad domains with different types of datasets to improve the association rule mining process. A case study to build a concrete regression model based on a real dataset is demostrated and the result shows the good performance of the proposed approach.

A broker in a market enables buyers and sellers to do business with each other and can provide many value-adding functions that cannot be replaced by direct buyer-seller dealings. Recently, some research has focused on this issue. However, broker modelling based on buyer's membership functions to carry out a matching process between buyer's requirements in fuzzy preference information and seller's offers is still sparse. Thus, this paper proposes membership function based matching approach of buyers and sellers through a broker in open emarketplace. The major contributions of this paper are that (i) a proposed framework is applicable to help a broker to carry out the matching process between buyers and sellers; (ii) a proposed method is to determine buyer's soft attribute weight by using association rule mining; and (iii) an objective optimization function and a set of constraints are built to help a broker to maximize buyer's total utility. Experimental results demonstrate the good performance of the proposed approach in terms of satisfying buyer's requirements and maximizing buyer's total utility.