Fuzzy IoT-driven smart waste management using advanced multi-criteria decision tools

Smart waste management (SWM) faces key challenges, including high operational costs, complex real-time monitoring, data management issues, and uncertain environmental impacts. The existing models fail to address these challenges due to a lack of reliability and sustainability concerns. A hybrid approach combining the Internet of Things (IoT) and fuzzy modeling is essential for dealing with these complex and uncertain issues in SWM. To overcome these limitations, this study develops a framework integrating q -rung orthopair fuzzy Z-numbers ( q -ROFZNs) with newly developed Aczél–Alsina based aggregation operators to enhance the representation and handling of uncertainty in multi-criteria decision making (MCDM) problems. The objective determination of criteria weights is achieved through the “Criteria Importance Through Intercriteria Correlation” (CRITIC) method, whereas the ranking of alternatives is obtained using the “Evaluation based on Distance from Average Solution” (EDAS) approach. An IoT-integrated CRITIC-EDAS framework is proposed for sustainable solid waste management (SWM) in Hamburg, Germany, enabling the identification of efficient and feasible waste management strategies. The proposed approach incorporates essential risk-related factors, including safety, economic impact, likelihood of occurrence, and detectability. Moreover, the reliability and applicability of the developed framework are demonstrated through sensitivity and comparative analyses, offering valuable insights for policymakers and supporting sustainable urban planning initiatives.