Algorithms for hesitant fuzzy soft decision making based on revised aggregation operators, WDBA and CODAS

Realistically, it isn't invariably the case that decision makers (DMs) or experts are capable of evaluating alternatives by means of exact values. With the uncertain information achieved, the DMs tend to offer their evaluations by giving various values in corresponding parameters. Hesitant fuzzy soft sets (HFSSs) permit each element to possess diverse number of parameter values of those parameters are denoted by multiple conceivable membership values. This paper develops three novel decision making methods in hesitant fuzzy soft text. First, the revised hesitant fuzzy aggregation operators are proposed for avoiding the counterintuitive phenomena. Later, the objective weights of diverse parameters are counted by deviation-based method. Then, we introduce the combination weights, which can reveal both the subjective decision information and the objective decision information. Afterwards, we present three methods for dealing hesitant fuzzy soft decision making issue via revised aggregation operators, WDBA (Weighted Distance Based Approximation) and CODAS (COmbinative Distance-based ASsessment). Finally, the feasibility and effectiveness of algorithms are stated by some numerical examples. The notable traits of the developed algorithms, compared to the existing hesitant fuzzy soft decision making algorithms, are (1) they can achieve the best alternative out of counterintuitive phenomena; (2) they have a stronger ability in differentiating the best alternative; (3) they can forbear the parameter selection issues.