The IoT-enabled sustainable reverse supply chain for COVID-19 Pandemic Wastes (CPW). (April 2023)

• Record Type: Journal Article
• Authors: Mosallanezhad, Behzad; Gholian-Jouybari, Fatemeh; Cárdenas-Barrón, Leopoldo Eduardo; Hajiaghaei-Keshteli, Mostafa
• Is Part Of: Engineering applications of artificial intelligence. Volume 120(2023)
• Publication Date: 2023-04
• Subjects: Supply chain design -- Reverse supply chain -- COVID-19 -- IoT -- Waste; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.105903
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Supply chains have been impacted by the COVID-19 pandemic, which is the most recent worldwide disaster. After the world health organization recognized the latest phenomena as a pandemic, nations became incapacitated to provide the required medical supplies. In the current situation, the world seeks an essential solution for COVID-19 Pandemic Wastes (CPWs) by pushing the pandemic to a stable condition. In this study, the development of a supply chain network is contrived for CPWs utilizing optimization modeling tools. Also, an IoT platform is devised to enable the proposed model to retrieve real-time data from IoT devices and set them as the model's inputs. Moreover, sustainability aspects are appended to the proposed IoT-enabled model considering its triplet pillars as objective functions. A real case of Puebla city and 15 experiments are used to validate the model. Furthermore, a combination of metaheuristic algorithms utilized to solve the model and also seven evaluation indicators endorse the selection of efficient solution approaches. The evaluation indicators are appointed as the inputs of statistical and multicriteria decision-making hybridization to prioritize the algorithms. The result of the Entropy Weights method and Combined Compromise Solution approach confirms that MOGWO has better performance for the medium-sizes, case study and an overall view. Also, NSHHO outclasses the small-size and large-size experiments. Highlights: Developing the reverse supply chain for COVID-19 wastes. Considering sustainable aspects in reverse supply chain design. Proposing an IoT platform to reinforce the reverse supply chain. A real case and a set of instance are developed to validate the model. Four metaheuristic algorithms are customized to solve the proposed model.