Multi-criteria group decision-making based on the combination of dual hesitant fuzzy sets with soft expert sets for the prediction of a local election scenario

Several existing group decision-making strategies and their hybrid models, such as soft expert sets (SESs), dual hesitant fuzzy sets (DHFSs), and hesitant fuzzy SESs, have proven to be valuable in resolving issues of many daily-life situations involving uncertainty. However, the hybridization of different existing uncertainty theories, particularly DHFSs and SESs, remains unexplored in the literature. To further enhance decision-making capabilities, the specific goal of this study is to extend the DHFS model to its extreme within the soft expert framework. For achieving this, we combine DHFSs with SESs as a new hybrid model called dual hesitant fuzzy soft expert sets (DHFSESs) to better handle uncertain situations in group decision-making. Furthermore, we investigate some basic properties and operations of the developed model and explain these notions with corresponding numerical examples, such as subset relation, complement, union, intersection, the ’OR’ operation, the ’AND’ operation, and level SESs of the developed DHFSESs. Additionally, we verify that the presented model obeys commutative, associative, and De Morgan’s laws. To demonstrate the practicality of the DHFSES model, we solve a real-world multi criteria group decision-making problem (prediction of results in an upcoming local election scenario) by incorporating dual hesitant fuzzy soft expert knowledge that is supported by an algorithm. Finally, we evaluate the authenticity and feasibility of the proposed DHFSES model by comparing it with some preexisting approaches, including dual hesitant fuzzy soft sets and hesitant fuzzy SESs, to validate its advantages over them.