Waste produced from medical facilities systems incorporates a blend of dangerous waste which can posture dangers to humans and ecological receptors. Lacking administration of healthcare waste can prompt hazard to medicinal service specialists, patients, public health, communities and the wider environment. Hence, proper management of healthcare waste is imperative to reduce the associated health and environment risk. In this paper, we extend the MULTIMOORA decision making method with intuitionistic hesitant fuzzy set to evaluate the healthcare waste treatment methods. Intuitionistic hesitant fuzzy set is a generalized form of a hesitant fuzzy set. Intuitionistic hesitant fuzzy set considers the uncertainty of data in a single framework and take more information into account. The MULTIMOORA method consists of three parts namely the ratio system, reference point approach and the full multiplicative form. In the optimal ranking methods, the IHF-MULTIMOORA method is uncomplicated it is able to be used practically with high dimension intuitionistic hesitant fuzzy sets. For pathological, pharmaceutical, sharp, solid and chemical wastes, the preferred waste disposal methods are deep burial, incineration, autoclave, deep burial, and chemical disinfection, respectively.

Wind energy is an energy source that is naturally clean, safe and cheap. It comes from a variety of sources. The electric energy generated by a wind turbine manifests as kinetic energy throughout the earth. The energy received from the wind is clean and is permanently available and can be generated forever. Turbine characteristics also have an impact on wind energy production. The turbine properties within a wind farm are important in estimating the load on power generation and wind turbine energy. The amount of energy released is calculated according to the type of the turbine model applied. In many situations, the choices of turbine model can incur various vague and complicated hesitation situations. To manage this situation, a hesitant fuzzy set with the Multi Criteria Decision Making (MCDM) is used. In the present research, the newly proposed Normal Wiggly Hesitant Fuzzy‐Criteria Importance Through Intercriteria Correlation (NWHF‐CRITIC) and Normal Wiggly Hesitant Fuzzy‐Multi Attribute Utility Theory (NWHF‐MAUT) methods were employed to rank turbine models based on quality, power level, voltage, and capacity. As part of this process, the NWHF method was utilized to extract and gather deeper information from the decision‐makers.