Abstract - This paper reports on a graph-based approach to modelling and solving combinatorial resource-constrained scheduling problem with respect to possibility to perform the individual activities in alternative ways (modes). These modes vary depending on processing time, time lags to other activities and resource requirements. A fuzzy scheduling problem can be formally defined by a number of activities-nodes that should be scheduled to minimize the project duration subject to generalized precedence relations, may require some units of limited in time use resources. Solution methodology is based on branch and bound procedure to operate with resource requirements and precedence constraints as well as selection of one single activity mode to construct a schedule.
Abstract - Financial interval time series (ITS) describe the evolution of the maximum and minimum prices of an asset throughout time, which can be related to the concept of volatility. Hence, their accurate forecasts play a key role in risk management, derivatives pricing and asset allocation, as well as supplements the information extracted by the time series of the closing price values. This paper proposes an interval fuzzy rule-based model (iFRB) for ITS forecasting. iFRB consists in a fuzzy rule- based approach with affine consequents, which provides a nonlinear method that processes interval- valued data. It is suggested as empirical application the prediction of the main index of the Brazilian stock market, the IBOVESPA. One-step-ahead interval forecasts are compared against traditional univariate and multivariate time series benchmarks and with an interval multilayer perceptron neural network in terms of accuracy metrics and statistical tests. The results indicate that iFRB provides accurate forecasts and appears as a potential tool for financial ITS forecasting.
Abstract - Linear Programming problems are solved in the present paper when costs are interval numbers and the comparison index based on the generalized Hukuhara difference is adopted to suggest the choice between two intervals that may have all the possible relative positions.
Abstract - Because of the unreliability of human experts judgements, fuzzy systems are widely applied in decision making problems related to adoption, implementation and maintenance of ERP systems. This paper presents a novel application of the fuzzy Analytical Network Process (ANP) for evaluating ERP post-implementation alternatives. The proposed framework aims at ranking different alternatives to implement a given post-implementation business requirement based on experts perception of implementation effort and risk. Regarding decision criteria, the ANP network considers two main levels. At the higher level we consider technical effort, organisational effort and long-term risk, whereas at the lower level we consider different strategies for implementing given business objects, functions and processes in an existing ERP system. Decision makers preferences are translated into preference weights using triangular fuzzy numbers. An example loosely based on a case study in a semiconductor company is presented to show the application of the proposed framework.
Abstract - We establish a correspondence between ideas from soft computing and social choice. This connection permits to draw bridges between choice mechanisms as well. Therefore we lay the grounds for new insights into soft-set-inspired decision making with a social choice foundation.
Abstract - This paper explores the applications of fuzzy logic inference systems as an instrument to perform linguistic analysis in the domain of prosodic prominence. Understanding how acous- tic features interact to make a linguistic unit be perceived as more relevant than the surrounding ones is generally needed to study the cognitive processes needed for speech understanding. It also has technological applications in the field of speech recognition and synthesis. We present a first experiment to show how fuzzy inference systems, being characterised by their capability to provide detailed insight about the models obtained through supervised learning can help investigate the complex relationships among acoustic features linked to prominence perception.