ARAS-H-IW: A Reproducible Hesitant Fuzzy Multi-Criteria Decision Framework with Inverse Weight Inference for Healthcare Waste Treatment Technology Assessment

Selecting appropriate healthcare waste (HW) treatment technologies is a challenging multi-criteria decision-making problem characterized by uncertainty, conflicting evaluation criteria, and limited decision-support information. Existing approaches often rely on subjective weighting schemes and may provide rankings that are sensitive to variations in expert judgments. This study proposes ARAS-H-IW, a hybrid decision-support framework that combines Additive Ratio Assessment under Hesitant Fuzzy Sets (ARAS-H) with an Inverse Weighting (IW) mechanism capable of inferring criterion weights directly from expert preference constraints through constrained quadratic optimization. To evaluate its practical applicability, the framework was applied to a real-world healthcare waste management case study using data provided by the Regional Health Directorate of Fez-Meknes (Morocco). A fully reproducible Python-based web platform was developed to automate the complete analytical workflow, including hesitant fuzzy modeling, multi-expert ranking aggregation, inverse weight inference, comparative evaluation, sensitivity analysis, Monte Carlo robustness assessment, and automated reporting. The proposed framework identified centralized autoclaving as the most favorable treatment alternative, followed by regional outsourcing and microwave disinfection. Comparative analyses with TOPSIS, VIKOR, PROMETHEE II, and EDAS showed strong agreement regarding the best and worst-ranked alternatives. Sensitivity and Monte Carlo analyses further demonstrated the stability and robustness of the obtained rankings, while all expert aggregation strategies converged toward the same consensus ordering. The results highlight the capacity of ARAS-H-IW to generate transparent, reproducible, and robust decision recommendations under uncertainty. The proposed framework provides a practical tool for healthcare waste technology assessment and offers a promising foundation for supporting evidence-based decision-making in regional healthcare waste management.