Optimizing a robust medical consortium emergency supply planning under probability uncertainty

Emergency supply planning (ESP) addresses the production and supply planning after the occurrence of emergency events. Since the outbreak of COVID-19, hospitals have faced a severe shortage of medical protective products. Therefore, ESP is an important research topic. In addition, the exact probability distribution of disaster occurrence is usually difficult to estimate due to limited data. For this purpose, this study adopts a pair of uncertainty sets to characterise the uncertain probability, where the nominal probability is estimated via the event tree analysis (ETA) method. Based on the constructed uncertainty sets, a novel two-stage globalised robust multi-objective ESP model is developed under the conditional value-at-risk (CVaR) criterion. A case study about a medical consortium in Tangshan, Hebei Province is conducted to demonstrate the advantages of the proposed optimisation method. Finally, according to the computational results of the numerical experiments and sensitivity analysis, some practical management implications are provided for decision-makers in management.