Combining consensus techniques and aggregation of decision makers´ judgments for supplier evaluation based on dual Hesitant Fuzzy theory

Abstract

Supplier evaluation plays an important role in performance management and supply chain development. In these decision making processes, several criteria, alternatives and decision makers should be considered, which could bring complexity to the problem. Several multi criteria techniques based on Fuzzy set theory are used to deal with imprecision inherent to decision-making process. In order to deal with the hesitations in the definition of linguistic terms and in the judgments of the decision makers simultaneously, the dual Hesitant Fuzzy representation has been studied for group decision making problems. Group decision making techniques are usually classified between consensus techniques or aggregation techniques; however, these approaches are used in different situations. Consensus, when it is desired to minimize the divergences between judgments; on the other hand, aggregation techniques seek to aggregate different opinions, without the minimization of divergences between opinions. However, no studies were found that seek to apply these approaches together. Thus, the main of this Phd project is to propose a model of group decision making that combine consensus approaches and the aggregation of judgments. The consensus will be applied in the initial stage, in the variables elicitation of decision process and respective weights; while aggregation will be used in the final stage of evaluation of the alternatives. In addition, in both steps, we propose the use of dual Hesitant Fuzzy representation. Pilot applications will be developed in an agroindustrial company with the purpose to evaluating the developed model. The project proposal includes five stapes: literature review; structuring and detailing the model; implementation of decision-making techniques; comparative analysis of the results and, pilot application. It is expected that this project will develop a model that is consistent with the needs of decision-making processes. This research proposal is linked to the research project INCT - INSID - National Institute of Information and Decision Systems. (AU)