基于梯形直觉模糊集的复杂产品模块划分

Complex Product Module Partitioning Based on Trapezoidal Intuitionistic Fuzzy Sets

为减少模块划分过程中存在的模糊性与不确定性,将模糊集理论引入到模块划分的多属性决策问题中,研究关于产品的结构、功能及客户参与度的关联关系定量化方法,采用基于梯形直觉模糊数计算加权复杂网络的边权;然后,采用FastUnfolding算法实现复杂机械产品零部件加权复杂网络的模块划分,得到模块划分的最优方案;最后,以二级减速器为例验证方法的实用性与有效性。

In order to reduce the ambiguity and uncertainty in the module partitioning process,the fuzzy set theory is introduced into the multi-attribute decision making problem of module partitioning.A quantitative method for the correlation of product structure,function and customer engagement is studied,and edge weights of weighted complex networks are calculated based on trapezoidal intuitionistic fuzzy numbers.Then,the Fast Unfolding algorithm is used to realize the module partitioning of the weighted complex network of complex mechanical products parts,and the optimal scheme of module division is obtained.Finally,the two stage reducer is taken as an example to verify the practicability and effectiveness of the method.