Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry

被引：151

作者：

Jain, Vipul ^{[1
]}

Sangaiah, Arun Kumar ^{[2
]}

Sakhuja, Sumit ^{[3
]}

Thoduka, Nittin ^{[3
]}

Aggarwal, Rahul ^{[3
]}

机构：

[1] Victoria Univ Wellington, Victoria Business Sch, Wellington, New Zealand

[2] VIT Univ, Sch Comp Sci & Engn, Vellore, Tamil Nadu, India

[3] GD Goennka World Inst & Lancaster Univ, Dept Mech Engn, Sohna, India

来源：

NEURAL COMPUTING & APPLICATIONS | 2018年 / 29卷 / 07期

关键词：

Supplier selection; AHP; TOPSIS; Consistency test; Sensitivity analysis; ANALYTIC HIERARCHY PROCESS; DECISION; SEGMENTATION;

D O I：

10.1007/s00521-016-2533-z

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Supplier selection is one of the key activities of purchase management in supply chain. Supplier selection is a multifaceted problem relating qualitative and quantitative multi-criteria. This paper deals with a supplier selection problem in an Indian automobile company. The work presents selection of headlamp supplier using integrated fuzzy multi-criteria decision-making approaches: analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). The selection process starts with identifying the criteria based on literature review and interviewing industry experts. Weights to criteria are assigned using AHP, and suppliers are ranked using AHP and TOPSIS. Consistency tests are carried out to check the quality of expert's inputs. Also, sensitivity analysis is done to check the robustness of the approach. The results address that fuzzy approaches could be effective and more accurate than the existing approaches for supplier selection problems.

引用

页码：555 / 564

页数：10

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