Decision support framework for healthcare waste disposal techniques assessment using an integrated picture fuzzy gained and lost dominance score-based approach

Improper management of healthcare wastes can lead to severe health hazard and environmental pollution such as water, air and soil. Selection of the suitable disposal technique requires consideration of multiple alternatives and evaluation criteria. Multi-criteria decision-making frameworks can be appropriate to handle the decision-making problem of healthcare waste disposal techniques assessment. However, uncertainty is inherently appeared in the assessment of such types of real-life applications. To this aim, the present work develops a hybrid picture fuzzy group decision-making framework and applies to assess healthcare waste disposal techniques based on various criteria and uncertainty perspective. In this regard, we introduce a new distance measure for picture fuzzy sets and further use to find the decision experts’ weights. The proposed measure can overwhelm the shortcomings of some of the previously introduced picture fuzzy distance measures. Based on the proposed measure, we further present an improved maximizing deviation model to derive the weight of criteria in the picture fuzzy environment. Further, we introduce a hybrid gained and lost dominance score method by incorporating the proposed distance measure-based weighting model for experts' weights and maximizing deviation method-based criteria weights. The developed framework considers the dominance relations between options and provides more efficient outcomes in the assessment of group decision-making problems. Finally, the developed method is applied to an empirical study of healthcare waste disposal techniques assessment problem, which illustrates the usefulness of introduced framework. Sensitivity analysis is performed to test different scenarios related to the proposed approach, which demonstrates the robustness of determined outcomes. Comparison with existing studies is discussed to show the robustness of the developed group decision-making framework. The present work contributes to the field of decision support through which the policymakers can take responsible decisions in the healthcare industry.