Collaborative Aircraft Sequencing Optimization on Crossed-runway Using Mixed Integer Programming

Abstract

In this study, a nonlinear mixed integer programming (NMIP) model is developed to solve an aircraft sequencing problem of coordinating arrival and departure in a multiple crossed-runway system. The objective is to minimize the overall aircraft delay, subject to aircraft types (light (L), medium (M), or heavy (H)), and specific separation time between two consecutive aircraft activities (departure or arrival) on crossed runways. The proposed NMIP model is further solved by using a genetic algorithm. Computational results show that the proposed approach provides a better coordinated sequence to reduce the overall aircraft delay compared to the commonly used first-come, first-served (FCFS) rule.

Lu He 何璐

Faculty of Supply Chain Management

My research interests include systematic resource optimization, multitask prediction, and predictive-driven mixed integer programming.