A hybrid optimization-simulation approach for robust weekly aircraft routing and retiming. (November 2017)

• Record Type: Journal Article
• Authors: Ben Ahmed, Mohamed; Ghroubi, Wisal; Haouari, Mohamed; Sherali, Hanif D.
• Is Part Of: Transportation research. Volume 84(2017)
• Page Start: 1
• Page End: 20
• Publication Date: 2017-11
• Subjects: Airline planning -- Robustness -- Aircraft maintenance routing -- Reformulation-Linearization Technique (RLT) -- Simulation; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2017.07.010
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: We propose a compact MINP model for the robust weekly aircraft maintenance routing problem. We apply the Reformulation–Linearization Technique (RLT) to provide an equivalent MILP formulation. We present a hybrid optimization-simulation approach to build enhanced robust aircraft routes. We present the results of an extensive computational study using real-data instances provided by a major international airline. Abstract: We address the robust weekly aircraft routing and retiming problem, which requires determining weekly schedules for a heterogeneous fleet that maximizes the aircraft on-time performance, minimizes the total delay, and minimizes the number of delayed passengers. The fleet is required to serve a set of flights having known departure time windows while satisfying maintenance constraints. All flights are subject to random delays that may propagate through the network. We propose to solve this problem using a hybrid optimization-simulation approach based on a novel mixed-integer nonlinear programming model for the robust weekly aircraft maintenance routing problem. For this model, we provide an equivalent mixed-integer linear programming formulation that can be solved using a commercial solver. Furthermore, we describe a Monte-Carlo-based procedure for sequentially adjusting the flight departure times. We perform an extensive computational study using instances obtained from a major international airline, having up to 3387 flights and 164 aircraft, which demonstrates the efficacy of the proposed approach. Using the simulation software SimAir to assess the robustness of the solutions produced by our approach in comparison with that for the original solutions implemented by the airline, we found that on-time performance was improved by 9.8–16.0%, cumulative delay was reduced by 25.4–33.1%, and the number of delayed passengers was reduced by 8.2–51.6%.