Developing IoT Sustainable Real-Time Monitoring Devices for Food Supply Chain Systems Based on Climate Change Using Circular Intuitionistic Fuzzy Set

Internet of Things (IoT) real-time monitoring devices, which compromise sustainable sensing parameter-based climate change, are developed to minimize food loss and waste to support supply chain systems during natural disasters. Numerous studies have shown that current IoT real-time monitoring devices offer remarkable prospects for future developments involving food supply chain systems with sustainable sensing parameters. Hence, modeling effective IoT real-time monitoring devices to minimize food loss and waste to support supply chain systems is crucial during natural disasters. This modeling process can be classified as multiple-attribute decision-making (MADM) given three issues: 1) the existence of multiple sensing parameter attributes; 2) the uncertainty related to the relative importance of these attributes; and 3) the variability of data. The present study endeavors to combine the fuzzy weighted with zero inconsistency method and circular intuitionistic fuzzy sets (C-IFS-FWZIC) with a new additive ratio assessment (ARAS) to determine ideal IoT real-time monitoring devices to minimize loss and waste and support food supply chain systems during natural disasters. The decision matrix for the study is built by intersecting 54 IoT real-time monitoring devices with ten sustainable sensing parameter attributes. The proposed method is further developed to ascertain the importance level of the sustainable sensing parameter attributes. These data are used in ARAS. Sensitivity analysis and correlation coefficient test are performed to assess the robustness of the proposed method.