A Heuristic and Exact Method: Integrated Aircraft Routing and Crew Pairing Problem

Mohamed, Nurul Farihan; Zainuddin, Zaitul Marlizawati; Salhi, Saïd; Mohamed, Nurul Huda; Mohamed, Nurul Akmal

doi:10.5539/mas.v10n4p128

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…For this reason, studies in which heuristic approaches are used in the solution of such large-scale problems are frequently encountered in the literature. Some of those approaches are Genetic Algorithm in Yang and Yang [18]; Particle Swarm Optimization in Mohamed et al [12], Sarhani et al [27], Afia and Sarhani [35]; Variable Neighbourhood Search in Al-Thani et al [24], Cui et al [38]; Ant Colony Optimization in Eltoukhy et al [37] and Simulated Annealing in Afsar et al [31].…”

Section: Discussionmentioning

confidence: 99%

“…The general approach in these studies is to use the exact methods that will provide the solution in a short time for small-scale problems prevailing integrated approach, the heuristic approach is used to check whether better solutions than those obtained in the sequential approach can be found. Parmentier and Meunier [13], Mohamed et al [12], and Mohamed et al [14] are other studies seeking integrated solutions to AMR and Crew Scheduling in problems with a weekly time horizon.…”

Section: Solution Methodsmentioning

confidence: 99%

“…Diaz-Ramirez et al[9], Weide et al[10], Dunbar et al[11], Mohamed et al[12], Parmentier and Meunier[13], Mohamed et al[14], Ahmed et al[15] √ √ Lacasse-Guay et al[16], Papakostas et al [17], Yang and Yang [18], Maher et al [19], Liang et al [20], Başdere and Bilge [21], Irvine et al [22], Gopalan [23], Al-Thani et al [24], Safaei and Jardine [25], Qin et al [26], Sarhani et al [27], Eltoukhy et al [28], Orhan et al [29], Aslamiah et al [30], Afsar et al [31], Kim et al [32], Bulbul and Kasımbeylı [33], Zhong et al [34], Afia and Sarhani [35], Zhang [36], Eltoukhy et al [37], Cui et al [38], Eltoukhya et al [39] √ Vos et al [40], Zhang et al [41], Dožic et al [42], Liu et al [43], Hu et al [44], Jufri et al [45], Lin and Wang [46] √ Akartunalı et al [47], Akartunalı et al [48], Burke et al [49], Chen et al [50], Sun [51], Jiang and Barnhart [52], Abdelghany et al [53], Sandamali et al [54], Zhao et al [55], Peng et al [56], Chen et al [57], Wang and Zhang [58], Ahmadian et al [59] √ Azadeh et al [60], Deng and Lin [61], Ionescu and Kliewer [62], Dück et al [63], Saddoune et al [64], Suraweera et al [65], Bayliss et al [66], Kasirzadeh et al [67], Lijima and Nishi [68], Arayikanon and Chutima [69] √ Özdemir et al [70], Pilla et al [71], Kang et al [72], Yang et al [73], Ma et al [74], Raudasoja [75], Boudia et al [76], Liu et al [77], Dozic et al [78], Note: EA: Ergonomic Assessment; MP: Maintenance Personnel; MTIS: Maintenance Tracking Info Systems; SI: Safety Issues; SRP: Schedule Recovery Problem…”

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confidence: 99%

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Aircraft Maintenance Routing Problem – A Literature Survey

Temucin

Tuzkaya

Vayvay

2021

PROMET

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The airline industry has shown significant growth in the last decade according to some indicators such as annual average growth in global air traffic passenger demand and growth rate in the global air transport fleet. This inevitable progress makes the airline industry challenging and forces airline companies to produce a range of solutions that increase consumer loyalty to the brand. These solutions to reduce the high costs encountered in airline operations, prevent delays in planned departure times, improve service quality, or reduce environmental impacts can be diversified according to the need. Although one can refer to past surveys, it is not sufficient to cover the rich literature of airline scheduling, especially for the last decade. This study aims to fill this gap by reviewing the airline operations related papers published between 2009 and 2019, and focus on the ones especially in the aircraft maintenance routing area which seems a promising branch.

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Section: Discussionmentioning

confidence: 99%

Section: Solution Methodsmentioning

confidence: 99%

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confidence: 99%

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Aircraft Maintenance Routing Problem – A Literature Survey

Temucin

Tuzkaya

Vayvay

2021

PROMET

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“…An integrated approach over all planning steps is discussed in [16,18]. Furthermore, [15] combine the aircraft routing problem with crew scheduling, while the CPP and the crew assignment problem is solved globally by [13].…”

Section: The Crew Pairing Problemmentioning

confidence: 99%

Optimizing Airline Crew Scheduling Using Biased Randomization: A Case Study

Agustín

Gruler

Armas

2016

Advances in Artificial Intelligence

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Various complex decision making problems are related to airline planning. In the competitive airline industry, efficient crew scheduling is hereby of major practical importance. This paper presents a metaheuristic approach based on biased randomization to tackle the challenging Crew Pairing Problem (CPP). The objective of the CPP is the establishment of flight pairings allowing for cost minimizing crew-flight assignments. Experiments are done using a real-life case with different constraints. The results show that our easy-to-use and fast algorithm reduces overall crew flying times and the necessary number of accompanying crews compared to the pairings currently applied by the company.

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A bi-objective aircraft maintenance routing problem based on flying hours to efficient use of available fleet

Esmaeilzadeh

Komijan

Kazemipoor

et al. 2022

JFM

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Purpose The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours threshold is met. After receiving maintenance service, the model ignores previous flying hours and the aircraft can keep on flying until the threshold value is reached again. Moreover, the model considers aircraft age and efficiency to assign them to flights. Design/methodology/approach The aircraft maintenance routing problem (AMRP), as one of the most important problems in the aviation industry, determines the optimal route for each aircraft along with meeting maintenance requirements. This paper presents a bi-objective mixed-integer programming model for AMRP in which several criteria such as aircraft efficiency and ferrying flights are considered. Findings As the solution approaches, epsilon-constraint method and a non-dominated sorting genetic algorithm (NSGA-II), including a new initializing algorithm, are used. To verify the efficiency of NSGA-II, 31 test problems in different scales are solved using NSGA-II and GAMS. The results show that the optimality gap in NSGA-II is less than 0.06%. Finally, the model was solved based on real data of American Eagle Airlines extracted from Kaggle datasets. Originality/value The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

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A Heuristic and Exact Method: Integrated Aircraft Routing and Crew Pairing Problem

Cited by 3 publications

References 16 publications

Aircraft Maintenance Routing Problem – A Literature Survey

Aircraft Maintenance Routing Problem – A Literature Survey

Optimizing Airline Crew Scheduling Using Biased Randomization: A Case Study

A bi-objective aircraft maintenance routing problem based on flying hours to efficient use of available fleet

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