Multi objective optimization model of outsourcing supplier portfolio selection for automotive industry based on lifecycle quality of economics

被引：0
|
作者：
Zhou F. [1 ]
Wang X. [2 ]
Zhou L. [3 ]
He Y. [4 ]
Ni L. [2 ]
Yang H. [5 ]
机构：
[1] School of Economics and Management, Zhengzhou University of Light Industry, Zhengzhou
[2] School of Mechanical Engineering, Chongqing University, Chongqing
[3] School of Management, Chongqing University of Technology, Chongqing
[4] Research Center of Modern Logistics, Graduate School at Shenzhen, Tsinghua University, Shenzhen
[5] Management Committee of Chongqing LiangLu-CunTan Free Trade Port Area, Chongqing
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 05期
关键词：
Automotive outsourcing; Economics of quality; Hybrid adaptive genetic algorithms; Lifecycle; Nonlinear multi- objective programming; Portfolio selection; Supplier; Total quality related cost;
D O I：
10.13196/j.cims.2019.05.022
中图分类号：
学科分类号：
摘要：
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. © 2019, Editorial Department of CIMS. All right reserved.
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页码：1259 / 1271
页数：12
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