Facile and optimal evaluation model of intelligent waste collection systems based on the Internet of Things: a new approach toward sustainability

Abstract

AbstractWaste management is a difficult and complicated issue. Since this waste may constitute a threat to persons and the environment, it is vital to guarantee that it is adequately collected. Therefore, new waste collection technologies that adopt modern technology and the Internet of Things (IoT) are the appropriate alternative. Determining the optimal intelligent technology for waste management and tuning its priorities is a complicated task that requires taking into account the dimensions of environmental, economic, and social sustainability. Thus, this paper introduces a hybrid methodology for multi-criteria decision-making (MCDM) that assesses intelligent waste management technologies utilizing IoT, taking into account multiple criteria. First, eleven decision-making criteria are determined to give a realistic approach. Second, the researchers demonstrated the innovative decision approach established on the combination of the Measurement of Alternatives and Ranking according to the COmpromise Solution (MARCOS) method and the Indifference Threshold-based Attribute Ratio Analysis (ITARA) method, called T2NN-ITARA, under a type-2 neutrosophic numbers (T2NNs) environment. This approach has been used to define the criteria’s relative significance. Also, T2NN-MARCOS approach has been established to evaluate and classify intelligent waste management technologies based on IoT and to reveal the most sustainable solution. An illustrative case study evaluating four intelligent waste management technologies based on IoT is presented to prove the validity of the applied methodology. The findings show that the criteria of sustainability and standardization are the two most influential criteria in the evaluation and classification of intelligent waste management technologies based on IoT. It has also been determined that the RFID and GPRS blend for waste management is the most suitable intelligent technology for garbage management. Sensitivity and comparison analyzes were also accomplished to illustrate the stability, strength, and robustness of the suggested approach. The research provides significant information for government and waste practitioners.