A mathematical programming model and dispatching rules for the repatriation program scheduling problem

Commercial flights were temporarily suspended due to the Corona Virus Disease (COVID-19) pandemic in March 2020. Subsequently, several countries had to repatriate their citizens from hosting countries. Typically, flights are planned ahead of time in commercial aviation, after which passengers buy their seats; however, in repatriation, the opposite happens. Citizens express their wish to return to their country first, and flights are scheduled accordingly. Due to the suspension of commercial aviation, decision makers had to rely on their flag-carrying airlines to schedule repatriation flights. These flights were constrained by limitations like the airline’s fleet capacity, the availability of quarantine locations, and pilots’ availability, while the decision makers’ objective was to return the most vulnerable citizens first. The present paper examines this problem, which we call the repatriation program scheduling problem (RPSP). The RPSP objective is to minimize the social impacts of keeping a country’s citizens abroad by assigning flights to different countries to repatriate stranded citizens. Therefore, we develop a mixed-integer linear program to model the RPSP and solve the RPSP using a commercial solver. We also suggest and test several dispatching rules that decision makers can use to schedule repatriation flights. We then model a case resembling an actual case and study the heuristics’ performances. Heuristics are found to lead to reasonable solutions for large quarantine capacities and limited pilot-airplane assignment options; otherwise, decision makers need to rely on exact solutions.