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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.

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.

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.

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.

This study aims to explore the interplay between CO2 emissions, financial development (FD) and foreign direct investment (FDI) in Asia-Pacific and Oceania. It also aims to understand short- and long-term impacts, emphasizing the role of FDI, FD and FD’s moderating effect on the FDI–CO2 relationship.

Using a 21-year panel data set (2000–2020) from 44 countries, the study employs the pooled mean group-autoregressive distributed lag (PMG-ARDL) model supplemented by the Dumitrescu–Hurlin panel causality test. This method assesses the complex dynamics and offers a robust analysis of short- and long-term effects in the Asia-Pacific and Oceanian context.

Long-term results indicate that FDI coupled with FD and FD’s moderating effect on FDI significantly contributes to CO2 emissions. Short-term relationships are more complex and lack statistical significance. FD positively moderates the FDI–CO2 relationship in the long run.

For investors, policymakers and stakeholders in Asia-Pacific and Oceania, the study highlights the importance of considering environmental impacts in investment decisions. The insights into the role of FDI and FD help craft policies and strategies for environmental sustainability.

Socially, this research emphasizes the necessity of a balanced approach to economic development, considering the potential long-term environmental consequences. Policymakers and stakeholders may use these findings to guide discussions and actions to achieve sustainable and socially responsible development in this dynamic region.

The findings contribute original insights into the essential relationships among FDI, FD and CO2 emissions in a diverse region like Asia-Pacific, enhancing the understanding of environmental implications in regions experiencing rapid economic growth.