Euclidean distance on treesoft sets and application for electric vehicle selection

Decision-making becomes more complex in situations involving uncertainty and multi-criteria structures, and classical methods prove inadequate for solving such problems. This study presents an adapted decision-making approach based on TreeSoft set theory, which is effective for modeling uncertainty and hierarchical relationships. The proposed method is based on the Euclidean distance measure defined on TreeSoft sets, which is an extension of the symmetric difference defined on soft sets. Thanks to the hierarchical structure of TreeSoft sets, the relationships between criteria are represented more accurately, providing a clearer perspective on the decision-making process. Adapting the Euclidean distance with symmetric differences increases the reliability of decisions. The proposed algorithm has been tested by applying it to the electric vehicle selection problem. The application results demonstrate that the developed method is both practically applicable and provides effective solutions to complex decision problems.

کلیدواژه ها:

Treesoft sets ، Decision-making ، Distance Measure ، Symmetric Differences ، Electric Vehicle