Optimization of a sustainable supply chain for medical device industry under uncertainty and COVID-19 pandemic

Regarding the importance of the supply chain design in its performance, and the necessity to consider uncertainty as its indispensable part, this paper explores the supply chain of medical devices under two scenarios of the inherent uncertainty of the parameters, such as fuzzy demand, and the conditions of the outbreak of the COVID-19 crisis. This study models a three-objective problem, to minimize the total cost, maximize the social effects, and minimize the environmental effects. Then, the formulated problem is solved by two different meta-heuristic algorithms, simulated annealing (SA) and grey wolf optimizer (GWO) algorithms, and the results of the numerical examples reveal that the GWO algorithm performs better than the SA algorithm. Moreover, the sensitivity analysis is conducted to explore the effects of uncertainty on the objective functions and it demonstrates that unlike the worst and average objective functions, which are lower under uncertainty, the best values are higher when uncertainty is considered. Finally, the effects of the increase in customer demand, hospital demand, and shortage cost sensitivity on the objective functions are analyzed. Pharmaceutical supply chains and the medical device industry can apply the findings and results of the current study to manage future unexpected uncertainties.