Conceptual design and configurations selection of S/H-UAVs based on Q-rung dual hesitant double layer FQFD

For the conceptual design phase of Unmanned Aerial Vehicles(UAVs),a process for conceptual design and configuration selection of Solar/Hydrogen powered UAVs(S/H-UAVs)is proposed.The design requirements of S/H-UAVs were analyzed firstly.The proposed process used Fuzzy Quality Function Deployment(FQFD)to establish logical and quantitative standards.Moreover,in order to appropriately describe the hesitancy of experts while making decision,it used Q-Rung Dual Hesitant Fuzzy Sets(QRDHFS)to score the correlationships.In addition,a decision-making framework is proposed to perform a logical selection of typical layouts based on defuzzi-fication method and Technique for Order Preference by Similarity to the Ideal Solution(TOPSIS).The present process has been applied for S/H-UAVs.The resulting set of design requirements con-sists of three categories:Mission Requirements(MRs),Engineering Characteristics(ECs)and Tech-nical Indicators(TIs).Four typical layouts of S/H-UAVs were sorted and determined.The performance of four typical layouts were evaluated and the Strut-Braced Wing(SBW)with external hydrogen storage was selected as the best layout for S/H-UAVs.

DUAL HESITANT FUZZY INFORMATION AGGREGATION WITH EINSTEIN T-CONORM AND T-NORM

Dual hesitant fuzzy set （DHFS） is a new generalization of fuzzy set （FS） consisting of two parts （i.e., the membership hesitancy function and the non-membership hesitancy fimction）, which confronts several different possible values indicating the epistemic degrees whether certainty or uncertainty. It encompasses fuzzy set （FS）, intuitionistic fuzzy set （IFS）, and hesitant fuzzy set （HFS） so that it can handle uncertain information more flexibly in the process of decision making. In this paper, we propose some new operations on dual hesitant fuzzy sets based on Einstein t-eonorm and t-norm, study their properties and relationships and then give some dual hesitant fuzzy aggregation operators, which can be considered as the generalizations of some existing ones under fuzzy, intuitionistic fuzzy and hesitant fuzzy environments. Finally, a decision making algorithm under dual hesitant fuzzy environment is given based on the proposed aggregation operators and a numerical example is used to demonstrate the effectiveness of the method.