This study analytically develops a new recovery planning optimisation model for managing the impacts of the recent COVID-19 outbreak for online business operations. Firstly, a mathematical model for the ideal plan is designed and then extended to generate a recovery plan in a finite planning horizon that maximises total profit. Recovery plans are generated considering two scenarios, namely the dynamic and uncertain situations. For the dynamic situation, a realistic system with time-dependent and dynamic demand, supply, and warehouse capacity for investigating the impacts of the COVID-19 outbreak is developed using several measures, such as collaborating with emergency suppliers, increasing warehouse capacity, and considering back-orders and lost sales to form recovery strategies. For the uncertain situation, demand, supply, and warehouse capacities are considered as uncertain variables. Further, an innovative solution approach using an adapted differential evolution technique, which is capable of (i) generating long-term recovery plans and (ii) solving both small- and large-scale problems, is developed. The results are illustrated using numerical analyses and simulation experiments. A sensitivity analysis is also conducted. In practice, the proposed optimisation model will assist the decision-makers of online business operations facing the COVID-19 outbreak to decide the optimal recovery plans.