Interval type-2 fuzzy TOPSIS method for calibration supplier selection problem: a case study in an automotive company

被引:11
|
作者
Toklu, Merve Cengiz [1 ]
机构
[1] Sakarya Univ, Dept Ind Engn, Esentepe Campus,M5 Block, Serdivan, Sakarya, Turkey
关键词
Supplier selection; Quality management; Calibration; TOPSIS; Fuzzy logic; GROUP DECISION-MAKING; CHAIN; MODEL; SETS;
D O I
10.1007/s12517-018-3707-z
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Product standardization has gained importance with the increase of customer demands and developments of technology. Products must be tested with measurement equipment to ensure quality standards. In quality management, the variability between products is targeted to be minimum. Products are produced at a target value with specific tolerance levels. Measurement equipment are used to confirm that the dimensions be in accordance with the required tolerance levels. The calibration of measurement equipment is crucial for acquiring certain measurements. The calibration process aims to minimize the measurement error by providing the accuracy of measurement equipment. The companies can carry out their calibration processes in the company structure for certain equipment. However, companies may need outsourcing for the calibration of complex equipment. This study includes a model to select the most appropriate calibration supplier by using the interval type-2 fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. The proposed approach is presented with a case study for torque-limiting wrench calibration in an automotive company. In the case study, ten different evaluation criteria were determined from the decision-makers and the literature for the calibration suppliers' evaluation and selection. Within the scope of these criteria, three alternative suppliers (A, B, and C) were evaluated and supplier C was selected as the most suitable calibration supplier.
引用
收藏
页数:7
相关论文
共 50 条
  • [1] Interval type-2 fuzzy TOPSIS method for calibration supplier selection problem: a case study in an automotive company
    Merve Cengiz Toklu
    [J]. Arabian Journal of Geosciences, 2018, 11
  • [2] Interval type 2-fuzzy TOPSIS and fuzzy TOPSIS method in supplier selection in garment industry
    Yildiz, Aytac
    [J]. INDUSTRIA TEXTILA, 2016, 67 (05): : 322 - 332
  • [3] Digital Supplier Selection for a Garment Business Using Interval Type-2 Fuzzy TOPSIS
    Ozbek, Ahmet
    Yildiz, Aytac
    [J]. TEKSTIL VE KONFEKSIYON, 2020, 30 (01): : 61 - 72
  • [4] Interval Type-2 Fuzzy Sets in Supplier Selection
    Tuerk, Seda
    John, Robert
    Oezcan, Ender
    [J]. 2014 14TH UK WORKSHOP ON COMPUTATIONAL INTELLIGENCE (UKCI), 2014, : 127 - 133
  • [5] Interval-valued Intuitionistic Fuzzy TOPSIS method for Supplier Selection Problem
    Tiwari, Ashutosh
    Lohani, Q. M. Danish
    Muhuri, Pranab K.
    [J]. 2020 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2020,
  • [6] The TOPSIS Method in the Interval Type-2 Fuzzy Setting
    Dymova, Ludmila
    Sevastjanov, Pavel
    Tikhonenko, Anna
    [J]. PARALLEL PROCESSING AND APPLIED MATHEMATICS, PPAM 2015, PT II, 2016, 9574 : 445 - 454
  • [7] A Combined Interval Type-2 Fuzzy MCDM Framework for the Resilient Supplier Selection Problem
    Hoseini, Seyed Amirali
    Hashemkhani Zolfani, Sarfaraz
    Skackauskas, Paulius
    Fallahpour, Alireza
    Saberi, Sara
    [J]. MATHEMATICS, 2022, 10 (01)
  • [8] An Integrated Best-Worst and Interval Type-2 Fuzzy TOPSIS Methodology for Green Supplier Selection
    Yucesan, Melih
    Mete, Suleyman
    Serin, Faruk
    Celik, Erkan
    Gul, Muhammet
    [J]. MATHEMATICS, 2019, 7 (02)
  • [9] Supplier selection using a clustering method based on a new distance for interval type-2 fuzzy sets: A case study
    Heidarzade, Armaghan
    Mandavi, Iraj
    Mandavi-Amiri, Nezam
    [J]. APPLIED SOFT COMPUTING, 2016, 38 : 213 - 231
  • [10] Strategic Decision Selection Using Hesitant fuzzy TOPSIS and Interval Type-2 Fuzzy AHP: A case study
    Sezi Cevik Onar
    Başar Oztaysi
    Cengiz Kahraman
    [J]. International Journal of Computational Intelligence Systems, 2014, 7 : 1002 - 1021