A hybrid optimization-simulation approach for robust weekly aircraft routing and retiming |
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| Affiliation: | 1. UR-OASIS, Ecole Nationale d’Ingénieurs de Tunis, Université de Tunis El Manar, 1002 Tunis, Tunisia;2. Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Qatar;3. Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, United States;1. Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar;2. UR-OASIS, Ecole Nationale d’Ingénieurs de Tunis, Université de Tunis El Manar, 1002 Tunis, Tunisia;1. Fuqua School of Business, Decision Sciences, Duke University, USA;2. Department of Industrial Engineering, Middle East Technical University, Turkey;3. Department of Industrial Engineering, Bilkent University, Turkey;1. Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China;2. Graduate Program in Operations Research, University of Texas, Austin, TX 78712-1063, USA;3. School of Economics and Management, Harbin Engineering University, Heilongjiang Harbin 150001, China;1. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong;2. College of Management, Shenzhen University, Shenzhen, China |
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| 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%. |
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| Keywords: | Airline planning Robustness Aircraft maintenance routing Reformulation-Linearization Technique (RLT) Simulation |
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