The new coronavirus disease 2019 (COVID‐19) has become a complex issue around the world. As the disease advancing and death rates are continuously increasing, governments are trying to control the situation by implementing different response policies. In order to implement appropriate policies, we need to consider the behavior of the people. Risk perception (RP) is a critical component in many health behavior change theories studies. People's RP can shape their behavior. This research presents a system dynamics (SD) model of the COVID‐19 outbreak considering RP. The proposed model considers effective factors on RP, including different media types, awareness, and public acceptable death rate. In addition, the simplifying assumption of permanent immunity due to infection has been eliminated, and reinfection is considered; thus, different waves of the pandemic have been simulated. Using the presented model, the trend of advancing and death rates due to the COVID‐19 pandemic in Iran can be predicted. Some policies are proposed for pandemic management. Policies are categorized as the capacity of hospitals, preventive behaviors, and accepted death rate. The results show that the proposed policies are effective. In this case, reducing the accepted death rate was the most effective policy to manage the pandemics. About 20% reduction in the accepted death rate causes about 23% reduction in cumulative death and delays at epidemic peak. The mean daily error in predicting the death rate is less than 10%.

Among the decisions related to the milk supply chain, those related to the supply of raw milk from farms to the dairy factories are highly important. In this paper, a two-stage scenario-based possibilistic model is developed for designing a milk supply chain network from farms to the dairy factory in the form of location-routing problem. The milk which is collected by collection center (CC) vehicles or directly is delivered by farmers to CCs. The occurrence of disruption is considered in the form of probable scenarios. A given percentage of capacity of CCs and some of the existing routes might be unavailable under each disruption scenario. A possibilistic programming method is used to cope with epistemic uncertainty in parameters (cost, demand, and milk produced). Because of the mathematical model's high complexity in large sizes, a Lagrangian relaxation algorithm is also devised. The proposed model helps to make optimal decisions in the milk collection process from farms to factories according to existing constraints. The numerical results show the efficiency of the solution approach.