the selection of healthcare waste treatment technologies by a multi-criteria group decision-making method with intuitionistic fuzzy sets

Nowadays, healthcare waste (hcw) management has been received attention by increasing the rate of the population and the usage of services. meanwhile, one of the significant challenges is to select the appropriate treatment technology for decision-makers (dms) in the hcw industry. in this respect, this paper proposes a new multi-criteria decision-making (mcdm) approach to compute criteria weights, dms' weights, and alternative ranking methods for assessing and selecting the best hcw treatment technology from various stakeholders. the proposed structure deals with uncertain evaluations of alternatives by using intuitionistic fuzzy (if)’ linguistic variables to show criteria weights and to extend two new weighting and ranking methods to obtain dms' weight and rank the hcw disposal alternatives based on uncertain conditions. eventually, an empirical case in shanghai, china, from the recent literature, is applied to determine the feasibility, validation, and effectiveness of the proposed model. results demonstrate that the introduced model is proper and efficient to handle the hcw treatment technology selection problem under an uncertain information condition. according to the final comparative results, the first alternative and the first dm have a high preference than others, respectively. furthermore, the sensitivity analysis determines that the final ranking results are reliable with changing the criteria' weights regarding four various kinds of states.