Blending Uncertainty Theory Innovative into Decision Support Framework for Selecting Agricultural Machinery Suppliers

Abstract

The rivalry among suppliers and stakeholders created pressure, making the selection of the best provider a crucial subject based on a set of criteria for the responsiveness of clients. Consequently, several criteria need to be considered to select the most suitable supplier. As a result, certain criteria may overlap and contradict one another. Multi-criteria decision-making (MCDM) techniques are widely used in many fields to address selection problems when there are numerous competing criteria and multiple alternatives. Hence, we are leveraging MCDM techniques in constructing a decision support framework (DSF) as MEthod based on the Removal Effects of Criteria (MEREC) and Multi-Attributive Border Approximation Area Comparison (MABAC). In our DSF, we harnessed the uncertainty theory of triangular neutrosophic sets (TrNSs) which is considered one of the advantages that DSF provides.TrNSs support experts in their judgments when facing problems such as imprecise judgments and insufficient data. As well, we applied the constructed DSF in a real case study for five suppliers for machinery agriculture and evaluated the suppliers based on ten criteria. The DSF’s findings indicated that supplier 5 is the optimal one for machinery agriculture otherwise supplier 2 is the worst one. Also, we applied another ranker technique of MCDM technique entailed in the weighted sum model (WSM) and compared WSM results with MABAC under the authority of TrNSs.