【摘要】 为了提升商用车轻量化优化效果，文中提出了一种基于熵权-改进逼近理想解排序法(TOPSIS)的驾驶室多目标决策方法。首先，对驾驶室有限元模型进行性能分析，并通过试验模态与仿真模态进行对比，验证了模型的准确性；其次，采用区域灵敏度分析，筛选出20个厚度和4个截面形状变量，并建立2阶响应面近似模型验证其精度；最后，采用第三代非劣排序遗传算法(NSGA-Ⅲ)对驾驶室进行多目标优化设计，再联合熵权-改进TOPSIS法求得非支配帕累托解的相对贴进度，并以此作为多目标决策的最终结果。结果表明：与优化前相比较，驾驶室质量减小了24.9 kg,减幅达到8.1%,能够满足轻量化和性能需求。更多还原

【Abstract】 In this article, for lightweight optimization of commercial vehicles, a cab multi-objective decision-making method is proposed based on entropy weight-improved TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution). Firstly, the performance of the cab’s finite-element model is analyzed; it is verified that this model is accurate by comparing the experimental modal with the simulated modal. Secondly, the regional-sensitivity analysis is used to select 20 thickness and 4 section shape variables; a second-order response-surface approximation model is set up to verify the accuracy. Finally, the third-generation non-inferior sorting genetic algorithm(NSGA-Ⅲ) is used to carry out the multi-objective optimization design on the cab; the entropy weight-improved TOPSIS method is used to obtain the relative schedule of the non-dominated Pareto solution, as the final result of multi-objective decision-making. The results show that the cab weight, compared with its counterpart before optimization, has reduced by 24.9 kg, which is a decrease of 8.1%, thus meeting the requirements of lightweight design and performance.更多还原