Integrating BWM and Aras Under Hesitant Linguistic Environment for Digital Supply Chain Finance Supplier Section

Abstract

In the era of intelligence and informatization, digital supply chain finance (DSCF) has become one of the important trends in the development of supply chain finance. With the gradual increase of DSCF suppliers and various requirements of small and medium-sized enterprises for suppliers in providing financing services, selecting the most suitable DSCF supplier is of great significance for most small and medium-sized enterprises to expand reproduction and improve competitiveness. To address such a decision-making problem, this paper proposes a new multi-expert multiple criteria decision-making model by integrating the Best Worst Method (BWM) and Additive Ratio ASsessment (ARAS) method under the hesitant fuzzy linguistic environment, in which the hesitant fuzzy linguistic BWM method is applied to determine the weights of criteria while the hesitant fuzzy linguistic ARAS method is proposed to rank the candidate suppliers. A case study is given to demonstrate the procedure of the proposed method for the selection of optimal DSCF suppliers, which shows the feasibility of the proposed method. Finally, sensitivity analysis and comparative analyses are provided to testify the applicability and superiority of the proposed method.

An Overview of Fuzzy Techniques in Supply Chain Management: Bibliometrics, Methodologies, Applications and Future Directions

“…Tuzkaya et al (2009) proposed a methodology utilizing a hybrid fuzzy ANP and fuzzy PROMETHEE approach to evaluate the environmental performance of suppliers. Liao et al (2019) integrated the BWM and ARAS under the hesitant linguistic environment to select digital SC finance suppliers. Liao et al (2020) applied different MCDM methods to promote the development of economy.…”

An overview of ARAS method: Theory development, application extension, and future challenge

“…In GDM environment, the evaluations are usually given by several experts with different knowledge backgrounds, and aggregating their evaluation information is one of the most important steps. For the studies on the ARAS method in GDM environment, there are mainly six different ways to aggregate individual evaluations, that is, (1) the arithmetic mean, 69,80,95,99 (2) the sets' characters-based method, 5,29,43,46,60,65,82,100 (3) Geometric Hesitant Fuzzy Linguistic Weighted Averaging (GHFLWA) operator, 8 (4) Interval-valued Intuitionistic Fuzzy Weighted Averaging (IIFWA) operator, 7,38 (5) entropy-based method, 101 and (6) Weighted Aggregated Sum Product Assessment (WASPAS) method 75 (see Table 4). Let assume that there are K experts.…”

“…Triangular fuzzy set The operation rules in Reference [108] Trapezoidal fuzzy set The operation rule in Reference [100] Gray set The operation rules in Reference [109] Intuitionistic fuzzy set The operation rules in Reference [90] Interval-valued triangular fuzzy set The operation rules in Reference [110] Interval-valued Intuitionistic fuzzy set The operation rules in Reference [111] Interval-valued Pythagorean fuzzy set The operation rules in Reference [112] Hesitant fuzzy linguistic term set The operation rules in Reference [113] Interval type-2 hesitant fuzzy set The operation rules in Reference [114] Rough set The operation rules in Reference [115] Probability multivalued neutrosophic set The operation rules in Reference [116] Z-number The operation rules in Reference [117] Picture fuzzy set The operation rules in Reference [118] information, some researchers converted the linguistic variables to triangular fuzzy number 23,27,29,31,34,41,43,46,48,50,51,60,70,72,76,80,82,84 some converted linguistic variables to gray numbers, 42,58,104 some converted the linguistic variables to rough numbers. 106 In addition, combining with the situation that experts are usually hesitant when giving their evaluations, Liao et al 8 extended the ARAS method to hesitant fuzzy linguistic environment and proposed a BWM-ARAS framework for finance supplier selection in digital supply chain, in which they used the operation rules for hesitan...…”

“…For the studies on supply chain management, most of them are related to supplier selection, such as selection of potential suppliers, 104 green supplier evaluation and selection, 46 selection of supplier with digital supply chain, 7,8,38 THK linear motion guide supplier selection, 72 resilient supplier selection, 31 and construction supplier selection. 54 Besides, Rostamzadeh et al 100 established a model to evaluate the supply chain management performance of small-mediumsized companies.…”

“…[1][2][3] For an MADM method, the two of the basic steps are: (1) collecting information. In this step, the alternatives' values under each attribute should be determined, which can be presented in various forms, such as crisp number, 4 fuzzy number, 5 gray number, 6 interval number, 7 linguistic term, 8 and so forth. Besides, the weights of attributes also need to be determined; (2) aggregating information through some specific methods to derive final results.…”