Contribution analysis of the cab-in-white for lightweight optimization employing a hybrid multi-criteria decision-making method under static and dynamic performance

Abstract

To improve the complexity of traditional variable screening and the optimization efficiency of automotive lightweight design, an objective evaluation and screening method is proposed, using a hybrid multi-criteria decision-making (HMCDM) method combined with contribution analysis. Multi-objective optimization design is conducted for the static and dynamic performance of the cab-in-white (CIW). An implicit parametric model of the CIW was built to define complex shapes and size variables. The HMCDM and technique for order of preference by similarity to ideal solution (TOPSIS) methods combined with regression analysis were used to determine the final optimization parameters of the CIW. Non-dominated sorting genetic algorithm-II was used to determine the optimal design parameters to improve the bending stiffness, torsion stiffness and mass of the CIW without reducing low-order natural frequencies. Compared with empirical and TOPSIS-based screening methods, HMCDM is closer to the design requirements. The CIW’s mass was reduced by 31.36 kg and performance was improved.