Aircraft re-routing optimization and performance assessment under uncertainty - Zhang, Xiaoge; Mahadevan, Sankaran (Decision Support Systems, 2017)
The need for aircraft re-routing arises when there is disruption in the system, such as when an airport is closed due to extreme weather. In this paper, we investigate a simulation-based approach to optimize the aircraft re-routing process, by considering multiple sources of uncertainty. The proposed approach has four main components: system simulation, uncertainty representation, aircraft re-routing algorithm, and system performance assessment. Several sources of uncertainty are accounted for in this approach, related to incoming aircraft, space availability in neighboring airports, radar performance, and communication delays. An aircraft re-routing optimization model is formulated to make periodic re-routing decisions with the objective of minimizing the overall distance travelled by all the aircraft, subject to the system resources. We analyze the performance of this aircraft re-routing system using system failure time as the metric. Since the simulation time is limited, right-censored data arises with respect to system failure time. A novel methodology is developed to compute the lower bound of system failure time in the presence of right-censored data, and to analyze the sensitivity of the system performance metric to the uncertain variables relating to the aircraft, radars, nearby airports, and communication system. Since the simulation is time-consuming, we build a Support Vector Regression (SVR) surrogate model to efficiently construct the system failure time distribution. •We model the aircraft re-routing using a dynamic simulation-based approach.•Multiple sources of uncertainty sources are considered in this process.•The proposed methodology provides novel insights into how uncertainty propagates through the system.•A data-driven sensitivity analysis is used to quantify the contribution of uncertain model inputs on the output variability.•A support vector regression surrogate model is constructed to predict the system performance distribution.