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The idea of sustainable development has been broadly discussed in a variety of businesses as concern for environmental protection and social responsibility has grown. Sustainable supply chain management (SSCM), an innovative management concept, has emerged and is currently a popular topic within industries. One of the most important challenges in SSCM is selecting sustainable suppliers. Hence several multi-criteria decision-making (MCDM) methodologies have already been presented to tackle this issue. However, a methodology that calculates sustainability indicators for every supplier and ranks them in relation to how close they are to an adjustable ideal solution would be of interest. This paper introduces a novel MCDM approach which calculates performance ratings using the likelihoods approach, creates sustainability indicators using likelihood-based performances, and ranks the suppliers according to their distance to ideal sustainability by using interval-valued intuitionistic fuzzy (IVIF) information. Owing to a better handling of subjective imprecision and more precise priorities, this innovative interval-valued intuitionistic fuzzy cumulative likelihood-based distance to ideal solution (IVIFCLDTIS) approach helps decisions become more realistic. A greater precision of priorities and a considerable decrease of pairwise comparison and computational complexity as compared to the earlier methods are achieved. To explain the implementation steps and benefits of the recommended method, an illustrative case regarding the supply chain for the construction sector is also solved. • A novel MCDM approach is proposed for sustainability performance evaluation of suppliers. • The concept of distance to ideal solution is proposed and generalized with likelihoods of IVIFSs. • Sustainability indicators are developed for suppliers using both cost/benefit criteria. • A two-mechanism multi-expert method is proposed to determine ranking orders. • A novel adjustable ideal solution is proposed to take into account visions and constraints of decision.
Hendiani et al. (Fri,) studied this question.