Integrating Quantitative and Qualitative Information in Design Optimization: A Soft Computing Approach

When solving a real life engineering design problem, engineers strive to ensure that the obtained results are sufficiently real in order to apply them in practice. The quest to achieve this realism in engineering solutions is still generating enormous research interest from various fields. It is widely accepted that Quantitative (QT) and Qualitative (QL) information is an intrinsic feature engineering design optimisation. However, it is surprising that the use QL models in design optimisation problem are not as common as QT models. This could become a problem if QT based models are not available or are partially defined. There are various instances where both type of information are equally important for decision-making and yet solutions are merely solved based only on the QT model. For example, in deciding possible solutions in terms of the likely quality of a hot rolled steel (perceived from the colour of the hot steel) and the cost of increasing the process performance. Here, practitioners reducing the problem formulation to cost related behaviour bias could end up with unrealistic solutions.