A new soft computing approach for green supplier selection problem with interval type-2 trapezoidal fuzzy statistical group decision and avoidance of information loss

Abstract

Green supplier selection problem (GSSP) is viewed as multiple attributes group decision-making (MAGDM) issue that includes the green growth and influential factors within subjective and objective natures. Because of the expanding uncertain conditions of social and economic environments, some assessment factors are not sufficiently described by numerical appraisals and classic fuzzy sets. Moreover, supply chain decision makers (DMs) may not provide complete rationality under numerous viable choice circumstances. In this research, a new MAGDM model is proposed by interval type-2 trapezoidal fuzzy numbers (IT2TrFNs) via some matrices of possibilistic mean and standard deviation statistical concepts. A new weighting method of experts within the group decision-making process is developed based on possibilistic statistical information. Also, a new ranking process based on relative-closeness coefficients is presented to rank all green supplier candidates under IT2TrF uncertainty. Finally, this research offers an illustrative example in supply chain networks to appraise green supplier candidates in terms of some factors by the proposed model along with the comparison to a recent decision method.