A Comparison of Promethee and TOPSIS Techniques Based on Bipolar Soft Covering-Based Rough Sets

Abstract

The uncertainty in the data is an obstacle in decision-making problems. In order to solve problems with a variety of uncertainties a number of useful mathematical approaches together with fuzzy sets, rough sets, soft sets, bipolar soft sets have been developed. The rough set theory is an effective technique to study the uncertainty in data, while bipolar soft sets have the ability to handle the vagueness, as well as bipolarity of the data in a variety of situations. This study develops a new methodology, which we call the theory of Bipolar soft covering-based rough sets (BSCB-RSs), which will be used to propose a new technique to solve decision-making problems. The idea introduced in this study has never been discussed earlier. Furthermore, this concept has been explored by means of a detailed study of the structural properties. By combining the BSCB-RSs model with two traditional decision-making methods (the PROMETHE-II method and the TOPSIS method), we introduce a novel method for addressing multi-criteria group decision-making (MCGDM) problems. We give an application in multi-criteria group decision making (MCGDM) to show that the proposed technique can be successfully applied to some real world problems including uncertainty, namely, the selection of site for renewable energy pro ject (Earth Dam). The effectiveness of the proposed method is validated by comparing it with existing methods. The showed techniques exhibit the practicability, feasibility and sustainability of Site selection. Both MCGDM methods give one Site as conclusion.