Bulanık küme ve fonksiyonların seçilen karar mühendisliği problemlerine uygulanması

Decision engineering is an emerging multidisciplinary field in which systems analysis, uncertainty handling and decision theory have been utilized in combination. A decision engineer provides with a set of computer-assisted mathematical models to understand and model complex decision problems encountered in real-life. These problems are mostly characterized by imprecision, complexity and dynamism. Understanding of complex interrelationships among decision criteria by managing complexity of the problem is of critical importance in real-life decision engineering problems. These interrelationships are hard to be articulated by means of exact terms. Interval type-2 fuzzy sets are more appropriate to model words due to their flexibility in assigning membership grades. First, interval type-2 fuzzy DEMATEL method has been used to demonstrate how the causal dependencies among criteria be managed within a hierarchically structured problem by means of interval type-2 fuzzy sets. Then, fuzzy cognitive maps, which are another advanced tools for modeling causal dependencies, are extended to the type-1 and interval type-2 fuzzy sets by proposing new alpha-cut based methods. Another critical issue in decision engineering is about intelligent processing of past data for dynamic decision support. As ever-more data pour through the networks of organizations, considerable effort has been made in order to cultivate valuable information from these resources. In this thesis, two new models, based on fuzzy cognitive maps and interval type-2 fuzzy regression functions, have been developed to solve real-life dynamic multiple attribute decision making problems. Therefore, this thesis contributes to the decision engineering literature with four new decision models.