Designing a robust logistics model for perishable emergency commodities in an epidemic outbreak using Lagrangian relaxation: a case of COVID-19

This paper proposes a three-echelon emergency commodities supply chain that considers location, allocation, distribution, and resource management planning to minimize the total cost when an epidemic occurs. The model takes into account some key aspects of the emergency distribution network in the event of an outbreak, such as the possibility of quarantining epidemic areas and delays in emergency commodity distribution, commodity storage due to panic buying, demand uncertainty, distribution time, and periodic review and updating of emergency commodity inventories. The model controls the remaining lifetime of the goods while taking into account their perishability. Various types of vehicles with differing capacities, transportation speeds, and costs are studied in order to achieve a suitable balance between cost and speed of delivering commodities. A robust possibilistic programming approach is used to deal with parameter uncertainty and a Lagrangian relaxation approach is used to solve the proposed model. A real case study on COVID-19 is presented in order to illustrate the validity of the suggested model as well as the effectiveness of the developed solution method, and a sensitivity analysis is performed. Based on the findings of this study, considering the uncertainties of system costs, demand, quarantine probability, and delays in the distribution of commodities have a significant impact on network costs during an epidemic outbreak and ignoring them leads to inaccurate estimates of system costs.