关键词: 全生命周期, 质量经济性, 总质量相关成本, 汽车外购件, 供应商, 组合优选, 非线性多目标规划, 混合自适应遗传算法

Abstract: As a crucial influential factor of vehicle quality,the quality of outsourcing parts in the automotive industry not only plays a significant role on vehicles performance and customer experience,but also influences warranty cost,brand reputation and sales amount in next lifecycle.To improve the economics of quality for self-owned brand automobile industry from warranty period perspective,a multi objective mixed integer nonlinear programming model was formulated to derive the optimal supplier portfolio of outsourcing parts by developing a hybrid genetic-based algorithm.The relative importance of key part was calculated by the improved Risk Priority Number (RPN) value concerning the nonlinear characteristics of  Failure Mode Effect Analysis (FMEA) ingredients,and the Taguchi method was employed to reflect hidden quality loss caused by customer complaints.The four items including total quality related cost,system reliability,delivery time and customer complaints were highlighted in the nonlinear multi objective programming model.The multi-attribute utility theory and combined weighting technique was used to integrate the sub-objective functions.To improve the efficiency of the genetic operations,a Hybrid Adaptive Genetic Algorithm (HAGA) was designed by integrating local search strategy to deal with the programming model.The numerical study demonstrated the effectiveness of the model and the advantages of the proposed algorithm,and the results could provide guidance to improve the quality of economics for automotive industries on outsourcing part procurement and supplier portfolio selection from total lifecycle perspective.

Key words: lifecycle, economics of quality, total quality related cost, automotive outsourcing, supplier, portfolio selection, nonlinear multi-objective programming, hybrid adaptive genetic algorithms