Procedure structuring for programming aircraft maintenance activities

Junqueira, Viviane Souza Vilela; Nagano, Marcelo Seido; Miyata, Hugo Hissashi

doi:10.1108/rege-02-2018-0026

Cited by 10 publications

(4 citation statements)

References 12 publications

(23 reference statements)

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“…Man-hour spending during aircraft maintenance is one of the critical parameters affecting the duration of the aircraft on the ground and flight safety (Knotts, 1999; Gramopadhye and Drury, 2000). Another parameter that affects aircraft maintenance duration is procedure structuring (Junqueira et al , 2020). Scheduling of maintenance operations on aircraft maintenance lines is a gap in the literature (Shaukat et al , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Determining aircraft maintenance times in civil aviation under the learning effect

Atıcı

Şenol

2022

AEAT

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Purpose Scheduling of aircraft maintenance operations is a gap in the literature. Maintenance times should be determined close to the real-life to schedule aircraft maintenance operations effectively. The learning effect, which has been studied extensively in the machine scheduling literature, has not been investigated on aircraft maintenance times. In the literature, the production times under the learning effect have been examined in numerous studies but for merely manufacturing and assembly lines. A model for determining base and line maintenance times in civil aviation under the learning effect has not been proposed yet. It is pretty challenging to determine aircraft maintenance times due to the various aircraft configurations, extended maintenance periods, different worker shifts and workers with diverse experience and education levels. The purpose of this study is to determine accurate aircraft maintenance times rigorously with a new model which includes the group learning effect with the multi-products and shifts, plateau effect, multi sub-operations and labour firings/rotations. Design/methodology/approach Aircraft maintenance operations are carried out in shifts. Each maintenance operation consists of many sub-operations that are performed by groups of workers. Thus, various models, e.g. learning curve for maintenance line (MLC), MLC with plateau factor (MPLC), MLC with group factor (MGLC) were developed and used in this study. The performance and efficiency of the models were compared with the current models in the literature, such as the Yelle Learning model (Yelle), single learning curve (SLC) model and SLC with plateau factor model (SLC-P). Estimations of all these models were compared with actual aircraft maintenance times in terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square of the error (MSE) values. Seven years (2014–2020) maintenance data of one of the top ten maintenance companies in civil aviation were analysed for the application and comparison of learning curve models. Findings The best estimations in terms of MAD, MAPE and MSE values are, respectively, gathered by MGLC, SLC-P, MPLC, MLC, SLC and YELLE models. This study revealed that the models (MGLC, SLC-P, MPLC), including the plateau factor, are more efficient in estimating accurate aircraft maintenance times. Furthermore, MGLC always made the closest estimations to the actual aircraft maintenance times. The results show that the MGLC model is more accurate than all of the other models for all sub-operations. The MGLC model is promising for the aviation industry in determining aircraft maintenance times under the learning effect. Originality/value In this study, learning curve models, considering groups of workers working in shifts, have been developed and employed for the first time for estimating more realistic maintenance times in aircraft maintenance. To the best of the authors’ knowledge, the effect of group learning on maintenance times in aircraft maintenance operations has not been studied. The novelty of the models are their applicability for groups of workers with different education and experience levels working in the same shift where they can learn in accordance with their proportion of contribution to the work and learning continues throughout shifts. The validity of the proposed models has been proved by comparing actual aircraft maintenance data. In practice, the MGLC model could efficiently be used for aircraft maintenance planning, certifying staff performance evaluations and maintenance trainings. Moreover, aircraft maintenance activities can be scheduled under the learning effect and a more realistic maintenance plan could be gathered in that way.

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Section: Literature Reviewmentioning

confidence: 99%

Determining aircraft maintenance times in civil aviation under the learning effect

Atıcı

Şenol

2022

AEAT

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“…Goldratt applied the Theory of Constraints (TOC) and introduced the critical chain scheduling and buffer management methodology [12]. This scheduling method is still used by many researchers due to its effective use [13][14][15][16][17][18]. This method is a competent approach to handling the activities duration risk as well as minimizing the makespan.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Scheduling Algorithm in Cloud-Based Additive Manufacturing; an Approach to Using Critical Chain Buffer Management Technique

Zohrehvandi,

Knapčíková

2024

Preprint

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In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

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“…As a result, the maintenance completion gets delayed, overall productivity remains low and costs overrun occurs frequently. According to [2], aircraft maintenance sector is extremely affected by fluctuations in the financial market and competitions. Hence, MRO organisations must reduce the maintenance costs and aircraft ground time for the maintenance.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Managing a Project Using Traditional Management Techniques and a Critical Chain Project Management Methodology in Aircraft Maintenance Field

Yadav¹,

Kulkarni²,

Yao³

2022

JTTs

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Aircraft on ground or down-time for its maintenance is a clear loss in revenue for an airline operation. With never ending competition and growing operating costs of aircraft, airlines continuously need to explore opportunities to reduce the aircraft down-time in order to remain sustainable. Due to round the clock operation feature of scheduled airlines, aircraft maintenance operations are generally carried out using traditional management methodologies instead of considering it as a project. Though aircraft heavy maintenance checks consist of several major tasks that can last from few weeks to a year, the maintenance organisations do not adopt modern project management methodologies. Therefore, this paper models a heavy maintenance check of an Airbus aircraft as a typical project and adopts contemporary project management methodology to explore the possibility of reducing the downtime. To this end, a case study has been done at an aircraft maintenance company to analyse the potential of this project management method in aircraft maintenance industry.

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Procedure structuring for programming aircraft maintenance activities

Cited by 10 publications

References 12 publications

Determining aircraft maintenance times in civil aviation under the learning effect

Determining aircraft maintenance times in civil aviation under the learning effect

A Heuristic Scheduling Algorithm in Cloud-Based Additive Manufacturing; an Approach to Using Critical Chain Buffer Management Technique

A Comparative Study of Managing a Project Using Traditional Management Techniques and a Critical Chain Project Management Methodology in Aircraft Maintenance Field

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