A Cloud Topsis Model for Green Supplier Selection

Abstract

Due to stringent governmental regulations and increasing consciousness of the customers, the present day manufacturing organizations are continuously striving to engage green suppliers in their supply chain management systems. Selection of the most efficient green supplier is now not only dependant on the conventional evaluation criteria but it also includes various other sustainable parameters. This selection process has already been identified as a typical multi-criteria group decision-making task involving subjective judgments of different participating experts. In this paper, a green supplier selection problem for an automobile industry is solved while integrating the Cloud model with the technique for order of preference by similarity to an ideal solution (TOPSIS). The adopted method is capable of dealing with both fuzziness and randomness present in the human cognition process while appraising performance of the alternative green suppliers with respect to various evaluation criteria. This model identifies green supplier S4 as the best choice. The derived ranking results using the adopted model closely match with those obtained from other variants of the TOPSIS method. The Cloud model can efficiently take into account both fuzziness and randomness in a qualitative attribute, and effectively reconstruct the qualitative attribute into the corresponding quantitative score for effective evaluation and appraisal of the considered green suppliers. Comparison of the derived ranking results with other MCDM techniques proves applicability, potentiality and solution accuracy of the Cloud TOPSIS model for the green supplier selection.

“A multi-tier sustainable food supplier selection model under uncertainty”

“…First, methods such as best–worst method (BWM) (Rezaei 2015 ; Ecer and Pamucar 2020 ; Torkayesh et al 2020a ), step‐wise weight assessment ratio analysis (SWARA) (Zolfani et al 2018 ), CRiteria Importance Through Intercriteria Correlation (CRITIC) (Diakoulaki et al 1995 ; Ghorabaee et al 2017 ), entropy (Lee and Chang 2018 ; Torkayesh et al 2020b ), analytic hierarchy process (AHP) (Yazdani et al 2020 ; Sambasivam et al 2020 ), analytic network process (ANP) (Asadabadi et al 2019 ), quality function deployment (QFD) (Yazdani et al 2017 ), data envelopment analysis (DEA) (Kumar et al 2014 ; Chu et al 2019 ), decision making trial and evaluation laboratory (DEMATEL) (Si et al 2018 ) are used to obtain the importance of decision criteria or to find the relationship between them. On other hand, ranking MCDM methods such as ELimination Et Choix Traduisant la REalité (ELECTRE) (Govindan and Jepsen 2016 ), Viekriterijumsko Kompromisno Rangiranje (VIKOR) (Opricovic and Tzeng 2004 ), TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) (Bai et al 2019 ), technique for order preference by similarity to ideal solution (TOPSIS) (Behzadian et al 2012 ; Ramakrishnan and Chakraborty 2020 ), combined compromise solution (CoCoSo) (Yazdani et al 2019a , b ), measurement of alternatives and ranking according to compromise solution (MARCOS) (Stević et al 2020 ; Chakraborty et al 2020 ), Grey rational analysis (GRA) (Kuo and Liang 2011 ), preference ranking organization method for enrichment evaluations (PROMETHEE) (Brans and Smet 2016 ) are used to prioritize a set of suppliers based on defined decision criteria. In Table 2 , MCDM methods for supplier selection problems in different industries are listed.…”

Fuzzy Optimization Model for Decision-Making in Supply Chain Management

“…Chakraborty et al [39] utilized D numbers-based Measurement Alternatives and Ranking according to the COmpromise Solution (MARCOS) method to developed a MCDM model to solve the supplier selection problem in the iron and steel industry. Ramakrishnan and Chakraborty [40] developed a cloud The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model for the green supplier selection process in the automobile industry. Ho et al [41] concluded that the most common criteria of supplier selection decision-making processes were quality, cost, management, technology, and flexibility.…”

Interval Valued T-Spherical Fuzzy Soft Average Aggregation Operators and Their Applications in Multiple-Criteria Decision Making

“…Here in this section, we will propose the comparative analysis of established work with other existing methods to prove the superiority of the present work. We will compare the present work with IVPFWA, IVPFOWA, IVPFHA, IVPFS f t WA, IVSFWA, IVSFOWA, IVSFHA, IVSFS f t WA, IVT-SFWA [36], IVT-SFOWA [36], IVT-SFHA [36], IVSFWAM [44], IVSFWGM [44], and IVPFS f t S [43]. Example 5.…”

“…Let = {0.28, 0.25, 0.23, 0.24} denote the weight vector of "e i " experts and p = {0.26, 0.20, 0.29, 0.25} denote the weight vector of "s j " parameters. We still use IVPFWA, IVPFOWA, IVPFHA, IVPFS f t WA, IVSFWA, IVSFOWA, IVSFHA, IVSFS f t WA, IVT-SFWA [36], IVT-SFOWA [36], IVT-SFHA [36], IVSFWAM [44], IVSFWGM [44], and IVPFS f t S [43] to compare with proposed work.…”

“…Since T-SFS is more general than SFS, so the concept of SFS f t S is further extended into a T-spherical fuzzy soft set T − SFS f t S proposed by Guleria et al [42]. Moreover, some new operations on interval-valued picture fuzzy soft set IVPFS f t are discussed in [43] and interval-valued spherical fuzzy weighted arithmetic means (IVSFWAM) and intervalvalued spherical fuzzy weighted geometric mean (IVSFWGM) operators are established in [44].…”