Double Decker Decision Framework with Fuzzy Data for Multi-Attribute Decision-Making

This paper presents a new framework for decision-making with fuzzy data. Specifically, in a double-decker setup, the first deck contains methods that perform rank value calculation of alternatives. The second deck fuses the rank order to provide a holistic ordering of alternatives. The first deck is highly scalable, as it can accommodate multiple approaches for ranking, and the final ordering is obtained by sending the rank orders from the first deck to the second—where the simple rank procedure is utilized. Earlier frameworks cannot perform rank fusion or use techniques like averaging and the Borda count without considering the personal choice of alternatives. This framework circumvents this challenge, and its usefulness is demonstrated through numerical examples.