Narrow Results

With the uncertain influential factors of demands and the lack of required historical data, demand estimation for new telecommunication services have generally relied just on marketing survey and analysis. However, the data collected from marketing survey are usually expressed in human linguistic forms and hence are fuzzy in nature. That means the estimation method derived from traditional sampling theory cannot fully represent such fuzzy data and thus biased consequences caused often. Therefore, in this study, to completely capture the uncertainty of the surveyed data, we adopt a series of analytical methods based on fuzzy set theory to construct a fuzzy estimation model. Based on the proposed model, a solution procedure is developed to aid users to acquire the demands of new telecommunication services. Finally, the solution procedure is employed to estimate demands of mobile phone service within one year in Taiwan with satisfactory results.

This paper presents a fuzzy set-based approach to the evaluation of information technology (IT) projects. We assume a multi-criteria decision-making framework, where sets of general and domain-specific criteria are used to judge the relative performance of alternative technologies. The methodology was originally developed for DIAS.net, an EU project aiming at the development of the Information Society in insular and isolated regions of Europe. In this paper, we present many aspects of our evaluation framework, including the synthesis of evaluation teams, the assessment of the importance of criteria, the evaluation of the performance of the alternatives and the final ranking and selection of projects. The methodology presented has the innovative feature of embodying techniques of fuzzy sets theory into the classical multi-criteria decision analysis. This combination enables us to handle efficiently the subjectiveness that often characterizes expert judgements on a decision problem. Fuzzy linguistic terms, such as "poor," "fair," "very important," etc. are proposed for assessing the relative merit of alternatives and criteria. The paper concludes by exploring the potentiality of the above methodology in providing a flexible and robust IT evaluation framework.

Bilevel programming techniques are developed for decentralized decision problems with decision makers located in two levels. Both upper and lower decision makers, termed as leader and follower, try to optimize their own objectives in solution procedure but are affected by those of the other levels. When a bilevel decision model is built with fuzzy coefficients and the leader and/or follower have goals for their objectives, we call it fuzzy goal bilevel (FGBL) decision problem. This paper first proposes a λ-cut set based FGBL model. A programmable λ-cut approximate algorithm is then presented in detail. Based on this algorithm, a FGBL software system is developed to reach solutions for FGBL decision problems. Finally, two examples are given to illustrate the application of the proposed algorithm.

In a bilevel decision problem, both the leader and the follower may have multiple objectives, and the coefficients involved in these objective functions or constraints may be described by some uncertain values. To express such a situation, a fuzzy multi-objective bilevel (FMOBL) programming model and related solution methods are introduced. This research develops a FMOBL decision support system through implementing the proposed FMOBL methods.

A fuzzy if-then rule whose consequent part is a real number is referred to as a simplified fuzzy rule. Simplified fuzzy if-then rules have been widely used in function approximation problems due to no complicated defuzzification is required. The proposed simplified fuzzy rule-based classification system, whose number of output is equal to the number of different classes, approximates an unknown mapping from input to desired output for each discriminant function. Not only a fuzzy data mining method is proposed to find simplified fuzzy if-then rules from training data, but also the genetic algorithm is employed to determine some user-specified parameters. To evaluate the classification performance of the proposed method, computer simulations are performed on some well-known datasets, showing that the generalization ability of the proposed method is comparable to the other fuzzy or nonfuzzy methods.

A new fuzzy mathematical programming model for supply chain planning under supply, process and demand uncertainty is proposed in this paper. A tactical supply chain planning problem has been formulated as a fuzzy mixed integer linear programming model where data are ill-known and modeled by fuzzy numbers with modified s-curve membership functions. The fuzzy model provides alternative decision plans to the decision maker (DM) for different degrees of satisfaction. Finally, the proposed model is tested by using data from a real automobile supply chain.

Since transactions may contain quantitative values, many approaches have been proposed to derive membership functions for mining fuzzy association rules using genetic algorithms (GAs), a process known as genetic-fuzzy data mining. However, existing approaches assume that the number of linguistic terms is predefined. Thus, this study proposes a genetic-fuzzy mining approach for extracting an appropriate number of linguistic terms and their membership functions used in fuzzy data mining for the given items. The proposed algorithm adjusts membership functions using GAs and then uses them to fuzzify the quantitative transactions. Each individual in the population represents a possible set of membership functions for the items and is divided into two parts, control genes (CGs) and parametric genes (PGs). CGs are encoded into binary strings and used to determine whether membership functions are active. Each set of membership functions for an item is encoded as PGs with real-number schema. In addition, seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. After the GA process terminates, a better set of association rules with a suitable set of membership functions is obtained. Experiments are made to show the effectiveness of the proposed approach.

A fundamental issue about installation of photovoltaic solar power stations is the optimization of the energy generation and the fault detection, for which different techniques and methodologies have already been developed considering meteorological conditions. This fact implies the use of unstable and difficult predictable variables which may give rise to a possible problem for the plausibility of the proposed techniques and methodologies in particular conditions. In this line, our goal is to provide a decision support system for photovoltaic fault detection avoiding meteorological conditions. This paper has developed a mathematical mechanism based on fuzzy sets in order to optimize the energy production in the photovoltaic facilities, detecting anomalous behaviors in the energy generated by the facilities over time. Specifically, the incorrect and correct behaviors of the photovoltaic facilities have been modeled through the use of different membership mappings. From these mappings, a decision support system based on ordered weighted averaging operators informs of the performances of the facilities per day, by using natural language. Moreover, a state machine is also designed to determine the stage of each facility based on the stages and the performances from previous days. The main advantage of the designed system is that it solves the problem of “constant loss of energy production”, without the consideration of meteorological conditions and being able to be more profitable. Moreover, the system is also scalable and portable, and complements previous works in energy production optimization. Finally, the proposed mechanism has been tested with real data, provided by Grupo Energético de Puerto Real S.A. which is an enterprise in charge of the management of six photovoltaic facilities in Puerto Real, Cádiz, Spain, and good results have been obtained for faulting detection.