Human Judgment Simulation and KDD Techniques in Automotive Platform Benchmark Selection

A robust-reliable decision-making approach to selecting benchmark platforms for developing an automotive family is addressed in this study. The main research activities include selecting the appropriate decision-making method, simulating possible scenarios to determine the relative importance of attributes based on experts and stakeholders, determining the decision space, valuing attributes based on databases, using knowledge discovery in databases (KDD) techniques, and performing statistical analysis and sensitivity assessments. This hybrid approach led to the reliable selection of five benchmark platforms with the highest level of desirability in terms of defined attributes and the highest level of robustness to uncertainties in the expert judgments and stakeholders' expectation levels.