Multi-objective discrete robust optimization for vehicle front-end structure design under pedestrian head impact

Vehicle front-end structures have drawn considerable attention for their significant advantages in the protection of head which is the most vulnerable body part in pedestrian accidents. Optimization design for vehicle front-end structures has proven rather essential and been extensively used to improve the performance of head protection. Nevertheless, an optimal design could become less meaningful or even unacceptable when some uncertainties present. Furthermore, the traditional discrete robust optimization is mostly focused on single objective problems. In fact, the design of front-end structures for pedestrian head impact is indeed a multi-objective discrete optimization problem. This study aimed to explore how to minimize the injury of the head involving uncertain environment and multi-objective discrete optimization problem. For this purpose, the paper proposes a novel multi-objective discrete robust optimization (MODRO) algorithm to minimize the injury of head involving uncertainties in pedestrian-vehicle collisions. MODRO algorithm is achieved by coupling grey relational analysis (GRA) and entropy method with Taguchi approach. Taguchi approach is utilized to perform experiments and analysis of means (ANOM) is used to predict the optimum design. The GRA and entropy method are used to transform multiple quality characteristics into a single performance indicator. The optimized result show that the MODRO algorithm significantly improved performance of pedestrian head protection and robustness of the vehicle front-end structure. The proposed algorithm can also be used to solve other complicated engineering problems.