A simulation-based optimization approach for multi-objective runway operations scheduling

This paper proposes a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling problem. The SbO approach utilizes a discrete-event simulation component to account for uncertain conditions, and an optimization component for finding the best-known Pareto set of solutions. This approach explicitly considers uncertainty to decrease the real operational cost of the runway operations as well as maximize fairness among aircraft as part of the optimization process. Due to the problem's large, complex, and unstructured search space, a hybrid Tabu/Scatter Search algorithm is developed to find solutions using an elitist strategy to preserve non-dominated solutions, a dynamic update mechanism to produce high-quality solutions, and a rebuilding strategy to promote solution diversity. The proposed algorithm maximizes both objectives of runway utilization and fairness. Computational experiments are conducted using real-life data sets for a major US airport to demonstrate that the proposed approach is effective and computationally tractable in a practical sense.