Viable healthcare supply chain network design for a pandemic.

The recent COVID-19 pandemic revealed that healthcare networks must have a flexible and effective structure. In this study, we develop a viable healthcare network design for a pandemic using a multi-stage stochastic approach. We propose a multi-level network that includes health centers, computed tomography scan centers, hospitals, and clinics. Patients have conditions to returning to normal life or quarantining at home. Three objectives are defined: maximizing the probability of patient recovery, minimizing the costs of all centers in the network, and minimizing the Coronavirus death rate. We investigate a real case study in Iran to demonstrate the model's applicability. Finally, we compare the healthcare supply chain network design in a pandemic with a normal situation to advise how the network can continue to remain viable.

Online hybrid Dutch auction approach for selling toxic assets under asymmetric bidders and the possibility of collusion

The COVID-19 pandemic-related economic recession causes a noticeable risk to banks with a substantial increase in toxic assets, including non-performing loans (NPLs). Despite the downsides, the pandemic is accelerating the spread of e-commerce. The best and most effective solution for resolving toxic assets would be a well-designed auction. Due to the fact, this represents a significant opportunity to tackle the next wave of NPLs via e-auctions. This study aims to model, for the first time, the Hybrid Dutch Auction (HDA) for online selling of NPLs. The HDA includes Dutch, Sealed-bid, and Best bid stages, and their equilibria are derived via backward induction. Theoretically and with a simulation, the study compares the HDA with the Amsterdam, the optimal auction, and the first-price and English auctions in symmetric and asymmetric environments. The results indicate that the HDA outperforms the other formats under different competition levels and the possibility of collusion.

R.Graph: A new risk-based causal reasoning and its application to COVID-19 risk analysis.

Various unexpected, low-probability events can have short or long-term effects on organizations and the global economy. Hence there is a need for appropriate risk management practices within organizations to increase their readiness and resiliency, especially if an event may lead to a series of irreversible consequences. One of the main aspects of risk management is to analyze the levels of change and risk in critical variables which the organization's survival depends on. In these cases, an awareness of risks provides a practical plan for organizational managers to reduce/avoid them. Various risk analysis methods aim at analyzing the interactions of multiple risk factors within a specific problem. This paper develops a new method of variability and risk analysis, termed R.Graph, to examine the effects of a chain of possible risk factors on multiple variables. Additionally, different configurations of risk analysis are modeled, including acceptable risk, analysis of maximum and minimum risks, factor importance, and sensitivity analysis. This new method's effectiveness is evaluated via a practical analysis of the economic consequences of new Coronavirus in the electricity industry.