Identifying potential routes and airports in Brazil: An integration of the route selection and fleet assignment problems

Abstract

The strategic planning of airline networks is critical for maximizing profitability and operational efficiency. Among the key challenges faced by airlines is the Route Selection Problem (RSP)—determining which routes to operate based on demand, economic conditions, and infrastructure—and the Fleet Assignment Problem (FAP)—optimally assigning aircraft to routes to minimize costs. While these problems are typically addressed separately, their integration can yield more robust and profitable solutions. This study presents a novel mathematical model that integrates RSP and FAP using Mixed Integer Linear Programming (MILP) to optimize network profitability and fleet utilization. The model was validated through a case study of a fictional regional airline in Southeast Brazil, analyzing 41 potential locations. The results identified 28 profitable routes, including 11 destinations currently without regular flights and one without an airport. By optimizing fleet allocation and route selection, the model provides a data-driven framework for airlines, policymakers, and investors to enhance network efficiency and identify underserved markets. This study demonstrates that an integrated approach to route selection and fleet assignment can significantly improve decision-making in the airline industry, offering a scalable methodology for network expansion and strategic investment.