The purpose of this paper is to obtain insights into the typology of uncertainty and relevant strategies adopted by manufacturing companies to achieve better supply chain flexibility. Strategies are classified into reactive (buffering) and proactive (redesigning). We develop a framework that links supply chain uncertainty, the two types of strategies for achieving supply chain flexibility and the relevant objectives to be achieved. Four case studies are compared in terms of uncertainty typology and strategies being adopted to improve supply chain flexibility. We present three propositions as a result of the study. First, to achieve better flexibility, companies have been focusing more on buffering than on proactive or redesign strategies. Second, companies tend to focus on internal operations rather than collaborating with external parties, and third, the power structure in a supply chain governs the type and configuration of supply chain flexibility.

Cat swarm optimization (CSO) is one of the new heuristic optimization algorithm which based on swarm intelligence. Previous research shows that this algorithm has better performance compared to the other heuristic optimization algorithms: Particle swarm optimization (PSO) and weighted-PSO in the cases of function minimization. In this research a new CSO algorithm for clustering problem is proposed. The new CSO clustering algorithm was tested on four different datasets. The modification is made on the CSO formula to obtain better results. Then, the accuracy level of poposed algorith was compared to those of K-means and PSO clustering. The modification of CSO formula can improve the performance of CSO clustering. The comparison indicates that CSO clustering can be considered as a sufficiently accurate clustering method.

In this paper, an important sampling method – Cross Entropy method is presented to deal with solving support vector machines (SVM) problem for multiclass classification cases. The use of this method is intended to accelarate the process of finding solution without sacrificing its quality. Using one-against-rest (OAR) and one-against-one (OAO) approaches, several binary SVM classifiers are constructed and combined to solve multiclass classification problems. For each binary SVM classifier, the cross entropy method is applied to solve dual SVM problem to find the optimal or at least near optimal solution, in the feature space through kernel map. For the meantime only RBF kernel function is investigated intensively. Experiments were done on four real world data sets. The results show one-against-rest produces better results than one-against-one in terms of computing time and generalization error. In addition, applying cross entropy method on multiclass SVM produces comparable results to the standard quadratic programming SVM in terms of generalization error

Climate change has significant impacts on changing precipitation patterns causing the variation of the reservoir inflow. Nowadays, Indonesian hydrologist performs reservoir inflow prediction according to the technical guideline of Pd-T-25-2004-A. This technical guideline does not consider the climate variables directly, resulting in significant deviation to the observation results. This research intends to predict the reservoir inflow using the statistical downscaling (SD) of General Circulation Model (GCM) outputs. The GCM outputs are obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR Reanalysis). A new proposed hybrid SD model named Wavelet Support Vector Machine (WSVM) was utilized. It is a combination of the Multiscale Principal Components Analysis (MSPCA) and nonlinear Support Vector Machine regression. The model was validated at Sutami Reservoir, Indonesia. Training and testing were carried out using data of 1991–2008 and 2008–2012, respectively. The results showed that MSPCA produced better extracting data than PCA. The WSVM generated better reservoir inflow prediction than the one of technical guideline. Moreover, this research also applied WSVM for future reservoir inflow prediction based on GCM ECHAM5 and scenario SRES A1B.