The assessment and selection of suppliers using AHP and MABAC with type-2 fuzzy numbers in automotive industry

Abstract

The selection of appropriate suppliers in an uncertain environment influences the sustainability and the competitive advantage of the automotive industry and hence presents one of the significant management problems. In the literature, it is suggested that an acceptable supplier may be determined concerning many criteria. In this manuscript, criteria selection is based on the relevant literature. Handling of different uncertainties is performed by using the type-2 fuzzy sets that have the capability of handling more considered impreciseness and uncertainties. In the presented research, the two-state model is proposed. In the first stage, the relative importance of criteria and sub-criteria are described by pre-defined linguistic expressions which are modeled by the interval type-2 triangular fuzzy numbers (IT2TFNs). The fuzzy weights vectors are given by using the fuzzy Analytic Hierarchical Process with IT2TFNs (IT2FAHP). After that, the ranking of suppliers is based on the modified Multi-Attributive Border Approximation area Comparison method (MABAC) with IT2TFNs (IT2FMABAC). The extension of the conventional MABAC includes: (a) modeling of sub-criteria values of IT2TFNs, (b) the fuzzy criteria values are calculated by using fuzzy algebra rules, (c) belonging of each supplier to border approximation areas (BAAs) is given by using procedure, and (d) the distance of suppliers to BAAs is determined by using the normalized Euclidean distance formulas. The proposed model is tested on the real-life data from the automotive supply chain. Through the presented research, it is shown that the proposed IT2FMABAC is a useful and reliable tool for the rational purchasing decision-making process.

Industrial and Management Applications of Type-2 Multi-Attribute Decision-Making Techniques Extended with Type-2 Fuzzy Sets from 2013 to 2022

“…This method is employed in several papers [58,71]. The aggregated fuzzy decision matrix is considered by Komatina et al [71] where the aggregation of attribute values is performed by using the order averaging operator extended with IT2TFNs [82]. The fuzzy decision matrix is stated in the other analyzed paper [58].…”

“…In both papers, the weighted no-aggregated/aggregated normalized decision matrix is given by using the procedure proposed by Chen and Lee [74] and the fuzzy algebra rules [75]. Additionally, the border approximation area matrix (BAA) is given by applying a fuzzy geometric mean [58,71].…”

“…Komatina et al [71] have proposed the determination of belonging to BAA areas based on their procedure. In this case, criteria function values for each supplier are determined by using Euclidean distance and fuzzy algebra rules [71]. The rank of suppliers is given by analogy to the procedure of conventional MABAC.…”

Selection of a Forklift for a Cargo Company with Fuzzy BWM and Fuzzy MCRAT Methods

“…It has been used, for instance, in performance assessment [45], 3PL selection [46], strategy selection [47], and transport assessment [48]. Numerous areas, including supplier selection [49,50], risk assessment [51], and infrastructure selection [52], have made significant use of fuzzy MABAC. Fuzzy MAIRCA has been used in the literature in various areas, such as vaccine selection [53], assessment of tourism potential [54], investment decisions [55], sustainability assessment [56], and material selection [57].…”

An Extended Multi-Attributive Border Approximation Area Comparison Method for Emergency Decision Making with Complex Linguistic Information

“…The multi-attributive border approximation area comparison (MABAC) is a new MCDM method recently put forward by Pamučar and Ćirović [34]. Its basic principle is to divide alternatives into the border, upper, and lower approximation regions, and compute the distances between all alternatives and the margin approximation matrix with respect to each criterion [35,36]. This approach makes the decision-making results as precise as possible by computing the potential gains and losses values [37].…”