A decision support system for risk reduction in pandemic spread based on the management of passenger air traffic

Abstract

A significant factor in the early spread of pandemics at an international level is passenger air traffic. Decisions regarding passenger air traffic could assist different countries in managing the risk of pandemic importation. However, flight cancelations would have economic and social impacts, leading to a multiobjective optimization problem. A decision support system (DSS) for reducing the risk of pandemic spread by managing passenger air traffic is introduced. This DSS enables decision makers (DMs) to parameterize the problem to be solved (time period, country of analysis, the percentage of targeted risk reduction, etc.), quantify DM preferences using ordinal information on the objectives, solve the resulting binary single-objective optimization problem using a binary particle swarm optimization metaheuristic, and visualize the optimal solution. The methodology is illustrated using the example of Spain with 38 national airports and 5000 international connections, involving 9678 flights within the time period from September 24 to October 7, 2020.