The novel coronavirus (COVID-19), which is one of its kind of humanitarian disasters, has affected people and businesses worldwide, triggering a global economic crisis. In this aspect, the tourism sector is not being left behind. The pandemic has not only affected the foreign exchange earnings (FEE) but also affected various regional developments, job opportunities, thereby disrupting the local communities as a whole. As there has been a substantial decline in the arrivals of overseas tourists in India in 2020, the paper aims to predict foreign tourists’ arrival in India and FEE using artificial neural networks (ANN). Furthermore, we analyse the impact of COVID-19 based on four scenarios considering with and without lockdown in terms of loss and gain in FEE. Lastly, the results obtained will help policymakers make necessary strategic and operational decisions, along with maximizing the FEE.

Agricultural supply chain (ASC) plays a vital role for sustainability as it is the main source of food supply. ASC encounters more sources of risk due to seasonality, perishability, and weather conditions, which makes the global food security system complex. This paper develops an optimization model for a perishable product supply chain to decide the optimal risk management strategy that maximizes the decision maker's expected profit under demand and price uncertainty. A base-case scenario is considered to show the impact of risk management strategy on performance improvement. The expected profit of the decision maker is obtained for different combination of strategies, and sensitivity analysis is performed to show the impact of perishability on the percentage of improvement from the base case scenario. The results show that backup supplier strategy is very effective during the yield disruption, but it is not as effective during harvest disruption. Hence, a single approach is inadequate to provide solution in all types of risk scenarios; thus, the combination of approaches is most effective.