Robust emergency logistics network design for pandemic emergencies under demand uncertainty

Abstract

Pandemic emergencies often result in a surge in demand for emergency supplies in affected regions. To expedite the recovery process, ensuring prompt and reliable emergency supplies is crucial, yet the demand for these supplies often exhibits stochastic and time-varying characteristics as the emergency evolves. This paper introduces a two-echelon multi-period location-routing allocation problem, with the objective of concurrent optimization of facility location, transportation routing, and the allocation of various types of emergency supplies. We integrate a constraint relationship equation that links supplies and cure rate into the established SEIR model to illustrate how emergency supplies counter the spread of the epidemic. In terms of the uncertain demand for various emergency supplies across multiple emergency periods, we quantify the nominal supply demand utilizing an enhanced SEIR model. On this basis, a set of uncertain demands for emergency supplies that are subject to random disturbances in real demand is established. We devise a multi-objective robust optimization model employing a budget-of-uncertainty robust approach to minimize total transportation time, total transportation cost, and shortage of emergency supplies. Subsequently, we develop a customized multi-objective discrete gray wolf algorithm aimed at enhancing solution efficiency. Our model is applied to a real-world case study in Shanghai during the 2022 COVID-19 pandemic. Results have proved that the algorithm is effective and feasible. Managerial insights are also provided.