A hybrid MCDM-based optimization method for cutting-type energy-absorbing structures of subway vehicles.

The structural optimal design, as an effective way to improve the crashing performance of energy-absorbing structures (EASs), still faces some challenges, e.g., multiple conflicting objectives and the non-uniqueness of Pareto solutions. To address these problems, this study proposes a hybrid optimization approach that combines the theories of multi-objective optimization and multiple-criteria decision-making to select the most preferred solution from the Pareto set with the consideration of engineering practitioners’ preferences. Specifically, this approach integrates the modified non-dominated sorting genetic algorithm (g-NSGA-II), fuzzy DEMATEL method, and GRA to handle the structural optimization problem in the field of passive safety protection for trains. First of all, a coupled thermal–structural finite element model of a cutting-type EAS is established and verified by experimental results. Next, a sensitivity analysis is conducted to explore the effects of knife count on the crashing performance of the structure and select the best alternative. After that, multi-objective optimization of this structure is performed using the proposed hybrid optimization method. Analysis and discussion are conducted to verify the effectiveness and practicability of this proposed approach. The optimization results show that the proposed conjoint method can be effectively employed to solve the multi-objective optimization problem of EASs. [ABSTRACT FROM AUTHOR]