Developing discrete optimization methods for a holistic airline scheduling algorithm with sustainability as an objective function

Abstract

This work outlines the investigation of discrete optimization methods employed to solve an airline fleet optimization and scheduling problem for a small network of airports operating B737-8200 MAX aircraft and approximately 5,000 passengers. The model developed in this study uses real-world inputs for technology, operations, policy and strategy to account for the current and future options available to the aviation sector. The model provides insight into the objective sustainability, assessed simultaneously from an economic and environmental perspective, of the decarbonization design space for the purpose of determining optimal pathways. Through the comparative analysis of the three different linear programming, discrete optimization methods in the solving of this type of problem, an understanding of the limitations and requirements of such a model are established. It was found that solutions from the model were highly solver and scenario dependent. Problems of larger size had difficulty obtaining optimal solutions within tolerance limits regardless of the linear programming solver used but resulting schedules did react in a deterministic way to scenario changes indicating viability as an assessment tool in the evaluation of decarbonization pathways.