The COVID-19 has placed pandemic modeling at the forefront of the whole world's public policymaking. Nonetheless, forecasting and modeling the COVID-19 medical waste with a detoxification center of the COVID-19 medical wastes remains a challenge. This current work presents a Fuzzy Inference System to forecast the COVID-19 medical wastes. Then, people are divided into four groups containing healthy people, suspicious, suspected of mild COVID-19, and infected. In this regard, a new fuzzy sustainable model for COVID-19 medical waste supply chain network for location and allocation decisions considering waste management is developed for the first time. Mixed-Integer Linear Programming is formulated to minimize the total costs, social responsibility, and environmental effects. To solve the proposed model, a new heuristic approach based on artificial intelligence methods, namely, the Grasshopper Optimization Algorithm and Tabu Search algorithm called Modified Grasshopper Optimization Algorithm and Tabu Search is developed. To show the performance of the suggested model, sensitivity analysis is performed on important parameters. A real case study in Iran/Tehran is suggested to validate the proposed model. Results show that the decision-makers should use an FIS to forecast COVID-19 medical waste and employ a detoxification center of the COVID-19 medical wastes to reduce outbreaks of this pandemic.
- Medical waste management
- Supply chain network
- Fuzzy inference system
- Artificial intelligence methods