Medical waste treatment station selection based on linguistic q-rung orthopair fuzzy numbers

Abstract

During the COVID-19 outbreak, the use of single-use medical supplies increased significantly. It is essential to select suitable sites for establishing medical waste treatment stations. It is a big challenge to solve the medical waste treatment station selection problem due to some conflicting factors. This paper proposes a multi-attribute decision-making (MADM) method based on the partitioned Maclaurin symmetric mean (PMSM) operator. For the medical waste treatment station selection problem, the factors or attributes (these two terms can be interchanged.) in the same clusters are closely related, and the attributes in different clusters have no relationships. The partitioned Maclaurin symmetric mean function (PMSMF) can handle these complex attribute relationships. Hence, we extend the PMSM operator to process the linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) and propose the linguistic q-rung orthopair fuzzy partitioned Maclaurin symmetric mean (Lq-ROFPMSM) operator and its weighted form (Lq-ROFWPMSM). To reduce the negative impact of unreasonable data on the final output results, we propose the linguistic q-rung orthopair fuzzy partitioned dual Maclaurin symmetric mean (Lq-ROFPDMSM) operator and its weighted form (Lq-ROFWPDMSM). We also discuss the characteristics and typical examples of the above operators. A novel MADM method uses the Lq-ROFWPMSM operator and the Lq-ROFWPDMSM operator to solve the medical waste treatment station selection problem. Finally, the usability and superiority of the proposed method are verified by comparing it with previous methods.