Algorithm and Implementation of MACBEV Method for Solving Large-data Problems

Group decision-making is now an essential approach in our daily lives. It plays a crucial role in the decision-making process. This compels certain human structures or decision-makers to seek external assistance in order to reach a consensus that is accepted by all stakeholders. This is why many multi-criteria decision-making methods have been developed and are widely used to clarify complex decision-making situations where intuition alone is insufficient. Among these existing methods, a new one has recently been developed, the scientific validity of which has been proven: the MACBEV method. It is obtained by hybridizing the EVAMIX method and the VMAVA+ voting method. The collective aggregation method based on the EVAMIX method and the VMAVA+ voting method (MACBEV) is one of these very recent methods that generates good properties but is unfortunately used to solve problems with small datasets where calculations are performed manually. Given the importance of the MACBEV method, it is essential to develop a computer program to broaden its scope. This will facilitate its application to concrete cases. In this work, we propose an algorithm and a computer program for this method that efficiently solves group decision problems, particularly large-scale problems whose manual processing is impractical. We then conduct a theoretical and graphical complexity study to demonstrate the efficiency of our program. Our computer model has been applied to large-scale data problems, and this has produced satisfactory results.