Automobile components procurement using a DEA-TOPSIS-FMIP approach with all-unit quantity discount and fuzzy factors

    Jiajia Chen   Affiliation
    ; Zeshui Xu Affiliation
    ; Xunjie Gou   Affiliation
    ; Dongbin Huang Affiliation
    ; Jianchuan Zhang   Affiliation


Components procurement is a crucial process in supply chain management of the automobile industry. The problem is further complicated by imprecise information and discount policies provided by suppliers. This paper aims to develop a computational approach for assisting automobile components procurement with all-unit quantity discount policy and fuzzy factors, from potential suppliers offering different product portfolios. We propose a two-stage approach consisting of a DEA-TOPSIS (data envelopment analysis procedures followed with a technique for order preference by similarity to an ideal solution) approach for screening suppliers, and subsequentially a fuzzy mixed integer programming (FMIP) model with multiple objectives for optimizing order allocations. The DEA-TOPSIS approach integrates suppliers’ comparative performance and diversity performance into an overall index that improves the ranking of potential suppliers, while the FMIP model features a soft time-window in delivery punctuality and an all-unit quantity discount function in cost. By applying it in a case of automobile components procurement, we show that this two-stage approach effectively supports decision makers in yielding procurement plans for various components offered by many potential suppliers. This paper contributes to integrating multi-attribute decision analysis approach in the form of DEA crossevaluation with TOPSIS and FMIP model for supporting components procurement decisions.

First published online 19 November 2020

Keyword : automobile components procurement, all-unit quantity discount, soft time-window, DEA cross-evaluation, TOPSIS, fuzzy mixed integer programming

How to Cite
Chen, J., Xu, Z., Gou, X., Huang, D., & Zhang, J. (2021). Automobile components procurement using a DEA-TOPSIS-FMIP approach with all-unit quantity discount and fuzzy factors. Technological and Economic Development of Economy, 27(2), 311-352.
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