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A model and optimization-based heuristic for the operational aircraft maintenance routing problem
Institution:1. School of Electronic and Information Engineering, Beihang University, 100191, Beijing, China;2. National Engineering Laboratory of Multi-Modal Transportation Big Data, 100191, Beijing, China;1. Data Science and Analytics, Scotiabank, 40 King St. W, Toronto M5H 1H1, ON, Canada;2. Department of Mechanical and Industrial Engineering, University of Toronto, 5 King''s College Road, Toronto M5S 3G8, ON, Canada;1. Department of Optimization, Zuse Institute Berlin, Takustr. 7, Berlin 14195, Germany;2. Department of Management Science, Lancaster University, Bailrigg, Lancaster LA1 4YX, UK;3. École Polytechnique de Montréal and GERAD, Department of Mathematics and Industrial Engineering, Montréal, Québec H3C 3A7, Canada
Abstract:This paper investigates the Operational Aircraft Maintenance Routing Problem (OAMRP). Given a set of flights for a specific homogeneous fleet type, this short-term planning problem requires building feasible aircraft routes that cover each flight exactly once and that satisfy maintenance requirements. Basically, these requirements enforce an aircraft to undergo a planned maintenance at a specified station before accumulating a maximum number of flying hours. This stage is significant to airline companies as it directly impacts the fleet availability, safety, and profitability. The contribution of this paper is twofold. First, we elucidate the complexity status of the OAMRP and we propose an exact mixed-integer programming model that includes a polynomial number of variables and constraints. Furthermore, we propose a graph reduction procedure and valid inequalities that aim at improving the model solvability. Second, we propose a very large-scale neighborhood search algorithm along with a procedure for computing tight lower bounds. We present the results of extensive computational experiments that were carried out on real-world flight networks and attest to the efficacy of the proposed exact and heuristic approaches. In particular, we provide evidence that the exact model delivers optimal solutions for instances with up to 354 flights and 8 aircraft, and that the heuristic approach consistently delivers high-quality solutions while requiring short CPU times.
Keywords:OR in airlines  Aircraft routing problem  Compact formulations  Very large-scale neighborhood search
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