Improved assessment model for health-care waste management based on dual 2-tuple linguistic rough number clouds

Abstract

The management of medical waste disposal is a major challenge faced by different cities in developing countries. The choice of optimal medical waste treatment technology is a complex multi-criteria group decision-making problem that requires consideration of multiple alternatives against conflicting criteria. Decision makers evaluating disposal municipalities may conduct random evaluations from different linguistic term sets according to different preferences and contexts. Various fuzzy numbers based approaches have been studied in HCWM (health-care waste management) process, however they have certain limitations such as lacking manipulation tools of diverse information, additional adjustments and suppositions, ignoring randomness and multi-granularity in experts’ judgements. In response, this research article proposes a novel mathematical models by integrating 2-tuple linguistic setting into rough approximations and cloud theory. First, three novel linguistic manipulation models, namely, 2-tuple linguistic clouds, 2-tuple linguistic rough numbers and dual 2-tuple linguistic rough number (D2tLRN) clouds are developed to handle uncertainty with randomness and multi-granularity simultaneously. Secondly, a hybrid weighting scheme is utilized to evaluate the relative importance of waste factors using both subjective and objective aspects of uncertainty. Thirdly, the proposed D2tLRN cloud model is integrated with the technique for order of preference by similarity to ideal solution (TOPSIS) method to design an evaluation approach for the selection of HCWM technologies. Finally, the application of the proposed method is studied with an empirical case study of health care waste management in the most crowded municipality. The sensitivity analysis of the proposed approach is also discussed in detail to study the effect of various parameters on the results. The out-performance and significance of the proposed D2tLRN cloud TOPSIS method is illustrated by comparing it with existing approaches.