随机环境下的航班调度、机队分配和飞机航线问题与代码共享协议的整合

IF 2.1 3区 工程技术 Q2 ENGINEERING, AEROSPACE
Kubra Kiziloglu, Ü. Sakallı
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引用次数: 0

摘要

航空公司必须进行资源管理以降低成本,这就需要解决一些优化问题,如航班计划、机队分配、飞机航线和机组人员调度等。这些问题带来了一些挑战。第一个挑战与独立解决这些问题的常见做法有关,由于这些问题相互关联,可能导致局部最优结果。第二个挑战在于与需求和非巡航时间等参数相关的内在不确定性。另一方面,航空公司可以采用一种称为代码共享的策略,即运营共享航班,以尽量减少这些挑战。在本研究中,我们引入了一个新颖的数学模型,旨在同时优化航班计划、机队分配和飞机航线决策,同时兼顾代码共享。该模型是一个三阶段非线性混合整数问题,随机参数代表需求和非巡航时间。对于较小规模的问题,优化软件可以有效地解决该模型。然而,随着航班数量的增加,传统软件就显得力不从心了。此外,考虑随机参数的多种情况可获得更稳健的结果,但由于优化软件的局限性,无法实现这一点。在这项工作中,我们介绍了两种新的基于模拟的元启发式算法,用于解决大维度问题,统称为 "模拟"。这些算法将蒙特卡罗模拟技术融入了模拟退火和布谷鸟搜索。我们将这些模拟算法应用于不同飞行规模和场景的各种问题样本。结果表明,我们提出的建模和解决方法能够在可接受的时间范围内有效地解决航班调度、机队分配和飞机航线问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic Environment
Airlines face the imperative of resource management to curtail costs, necessitating the solution of several optimization problems such as flight planning, fleet assignment, aircraft routing, and crew scheduling. These problems present some challenges. The first pertains to the common practice of addressing these problems independently, potentially leading to locally optimal outcomes due to their interconnected nature. The second challenge lies in the inherent uncertainty associated with parameters like demand and non-cruise time. On the other hand, airlines can employ a strategy known as codesharing, wherein they operate shared flights, in order to minimize these challenges. In this study, we introduce a novel mathematical model designed to optimize flight planning, fleet assignment, and aircraft routing decisions concurrently, while accommodating for codesharing. This model is formulated as a three-stage non-linear mixed-integer problem, with stochastic parameters representing the demand and non-cruise time. For smaller-scale problems, optimization software can effectively solve the model. However, as the number of flights increases, conventional software becomes inadequate. Moreover, considering a wide array of scenarios for stochastic parameters leads to more robust results; however, it is not enabled because of the limitations of optimization software. In this work, we introduce two new simulation-based metaheuristic algorithms for solving large-dimensional problems, collectively called “simheuristic.” These algorithms integrate the Monte Carlo simulation technique into Simulated Annealing and Cuckoo Search. We have applied these simheuristic algorithms to various problem samples of different flight sizes and scenarios. The results demonstrate the efficacy of our proposed modeling and solution approaches in efficiently addressing flight scheduling, fleet assignment, and aircraft routing problems within acceptable timeframes.
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来源期刊
Aerospace
Aerospace ENGINEERING, AEROSPACE-
CiteScore
3.40
自引率
23.10%
发文量
661
审稿时长
6 weeks
期刊介绍: Aerospace is a multidisciplinary science inviting submissions on, but not limited to, the following subject areas: aerodynamics computational fluid dynamics fluid-structure interaction flight mechanics plasmas research instrumentation test facilities environment material science structural analysis thermophysics and heat transfer thermal-structure interaction aeroacoustics optics electromagnetism and radar propulsion power generation and conversion fuels and propellants combustion multidisciplinary design optimization software engineering data analysis signal and image processing artificial intelligence aerospace vehicles'' operation, control and maintenance risk and reliability human factors human-automation interaction airline operations and management air traffic management airport design meteorology space exploration multi-physics interaction.
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