This study develops a mathematical model to mitigate disruptions in a three-stage (i.e., supplier, manufacturer, retailer) supply chain network subject to a natural disaster like COVID-19 pandemic . This optimization model aims to manage supply chain disruptions for a pandemic situation where disruptions can occur to both the supplier and the retailer . This study proposes an inventory policy using the renewal reward theory for maximizing profit for the manufacturer under study . Tested using two heuristics algorithms, namely the genetic algorithm (GA) and pattern search (PS), the proposed inventory-based disruption risk mitigation model provides the manufacturer with an optimum decision to maximize profits in a production cycle . A sensitivity analysis was offered to ensure the applicability of the model in practical settings . Results reveal that the PS algorithm performed better for such model than a heuristic method like GA . The ordering quantity and reordering point were also lower in PS than GA . Overall, it was evident that PS is more suited for this problem . Supply chain managers need to employ appropriate inventory policies to deal with several uncertain conditions, for example, uncertainties arising due to the COVID-19 pandemic . This model can help managers establish and redesign an inventory policy to maximize the profit by considering probable disruptions in the supply chain network.