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A model and optimization-based heuristic for the operational aircraft maintenance routing problem

机译:基于模型和优化的启发式飞机维修航线问题

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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 neighbor-hood 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. (C) 2016 Elsevier Ltd. All rights reserved.
机译:本文研究了飞机运行维护路由问题(OAMRP)。给定针对特定同质机队类型的一组航班,此短期规划问题需要构建可行的飞机路线,以完全覆盖每个航班一次并满足维护要求。基本上,这些要求使飞机在累积最大飞行小时数之前必须在指定的站点进行计划的维护。这个阶段对航空公司而言意义重大,因为它直接影响机队的可用性,安全性和盈利能力。本文的贡献是双重的。首先,我们阐明了OAMRP的复杂性状态,并提出了一个精确的混合整数规划模型,该模型包括变量和约束的多项式。此外,我们提出了一种图形简化程序和有效不等式,旨在改善模型的可解性。其次,我们提出了一种非常大规模的邻域搜索算法,以及一个计算紧密下界的过程。我们介绍了在真实世界的飞行网络上进行的大量计算实验的结果,并证明了所提出的精确和启发式方法的功效。特别是,我们提供的证据表明,精确的模型可为多达354个航班和8架飞机的实例提供最佳解决方案,并且启发式方法始终可在要求较短CPU时间的情况下始终提供高质量解决方案。 (C)2016 Elsevier Ltd.保留所有权利。

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