No abstract

Today with swift growing of plastic cards industry in the world, variety and volume of data stored in the database is growing strongly, this issue reminds the growing need of banks and financial institutions in applying knowledge discovery processes on value creation services. The original approach of this paper, is step by step implementing process of data mining in real-life transaction of debit cards, with the aim of customer profiling. In this study profiling is applied with two approaches of explorative and predictive analysis. In explorative model SOM and TwoStep clustering techniques are used. Also in predictive model four decision tree techniques are applied, the C5.0, Chi-square Automatics Interaction Detection (CHAID), Quest, classification and regression. Finally, the optimal models details are more analyzed to discover the knowledge in transactions done.

This paper presents a systematic approach for evaluating the performance of a project based organization. We applied a two level fuzzy Data Envelopment Analysis (DEA) technique in project based organizations. In order to determine the required inputs and outputs, important indicators have selected using both expert judgments and statistical analysis. Then the two-level DEA model is successfully adapted. In this model by considering the outputs through a hierarchical process, a large number of sub indicators have provided and then rolled up to the higher level. Since the exact amount cannot be attributed to the indicators and they includes interval of values during the project life cycle, the interval DEA model will be discussed as a model help to determine the most preferred solution. At the end, some of the projects have been successfully evaluated throughout the approach proposed in this paper.

This research consider a project scheduling problem with the object of minimizing weighted earliness-tardiness penalty costs, subject to precedence relations among the activities and resource-constrained. Project activities are assumed to have a known deterministic due date, a unit earliness as well as a unit tardiness penalty cost and constant renewable resource requirements. The objective is to schedule the activities in order to minimize the total weighted earliness–tardiness penalty cost of the project subject to the finish–start precedence constraints and the constant renewable resource availability constraints. With these features the problem becomes highly attractive in just-in-time environments. A genetic algorithm (GA) is proposed to solve this model. In addition design of experiments and response surface methodology are employed to tune the GA parameters and to evaluate the performance of the proposed method in 270 test problems. The results of the performance analysis will be shown at the end of this paper.