Wednesday , July 28 2021

Automotive Parts Purchasing Using the Fuzzy MOMIP Model of Reliability Objective with Uncertain Weights

Zeshui XU1*, Jia jia CHEN1,2, Jianmei YE1,2, Jianchuan ZHANG3
1 Business School, Sichuan University, No. 24 South Section 1 Yihuan Road, Chengdu, 610064, China
xuzeshui@263.net (*Corresponding author), jiajia_818@126.com
2 School of Economics and Management, Chongqing University of Posts and Telecommunications,
No. 2 Chongwen Road, Nan’an District, Chongqing, 400065, China
yejianmei2423@163.com 

3 SOKON Industry Group Stock Corporation Ltd, No. 61-1 Jinqiao Road, Shapingba District,
Chongqing, 400033, China
jianchuan@126.com

Abstract: This paper develops an effective order allocation method considering a reliability objective, fuzzy information provided by candidate suppliers and uncertain objective weights, and uses it to provide automotive parts procurement solutions. A fuzzy multi-objective mixed integer programming (MOMIP) model with uncertain objective weights is formulated to minimize total cost, the unqualified automotive parts and to maximize supply reliability, obtained by a synthetical evaluation of five criteria including financial status stability, technique of product reliability, quality reliability, service and environment sustainability. An extended interactive algorithm is developed to solve the model. By applying it in a case of sensor parts purchasing under an operational context of industry 4.0, the result shows that the reliability objective is effective in supplier selection and order allocations; and that the interactive algorithm only requiring the preference order on the objective weights from decision makers is also effective.

Keywords: Fuzzy MOMIP model, Reliability objective, Uncertain objective weights, Extended interactive algorithm, Industry 4.0.

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CITE THIS PAPER AS:
Zeshui XU, Jia jia CHEN, Jianmei YE, Jianchuan ZHANG, Automotive Parts Purchasing Using the Fuzzy MOMIP Model of Reliability Objective with Uncertain Weights, Studies in Informatics and Control, ISSN 1220-1766, vol. 30(2), pp. 5-20, 2021. https://doi.org/10.24846/v30i2y202101