A multi‐objective transportation model for COVID‐19 patients: lesson learned from France

Abstract The current COronaVIrus Disease 2019 (COVID‐19) pandemic has revealed many challenges to the world. The pandemic obliged public authorities to impose lockdowns and stop several activities. Limited hospital capacity was considered one of the main reasons behind lockdown decisions. To partially face hospital congestion, the healthcare authorities suggested transferring patients to hospitals in regions with available beds to relieve overloaded hospitals. Such transfer operations required exceptional modes of transportation such as trains and airplanes. This paper aims to demonstrate that efficient health logistics might help face the pandemic health effects and save lives. In this paper, we propose a multi‐objective mathematical program to schedule the transfer of patients using trains and airplanes. The proposed sustainable logistics model was tested using French real‐life data for the period March–April 2020 using CPLEX software. We also illustrate the health benefits of our solution.