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BOEING 777 A-BAKIM ARALIĞI İÇİN TAGUCHI METODU TABANLI ADAM-SAAT OPTİMİZASYONU

Year 2025, Volume: 24 Issue: 47, 201 - 216, 30.06.2025

Cahit Bilgi , Can Eyüpoğlu , Abdullah Bülbül

https://doi.org/10.55071/ticaretfbd.1633385

Abstract

Uçak bakımı, emniyet ve operasyonel verimlilik açısından kritik öneme sahiptir. Boeing 777’de A-bakımları her 1000 uçuş saatinde (Flight Hours-FH) bir yapılmakta, bu da uçak başına yılda beş bakım yapılmasına neden olmakta ve önemli arıza süreleri ve işçilik maliyetleri ortaya çıkmaktadır. Bu çalışma, Taguchi yöntemi tabanlı bir adam-saat optimizasyon modeli önererek A-bakım aralığını 1500 FH’ye çıkarmakta ve emniyet ile mevzuata uygunluğu korurken yıllık bakım sayısını azaltmaktadır. Bu çalışmada gerçekleştirilen analiz, bu aralığın uzatılmasının önemli faydalarını ortaya koymuştur. Yıllık arıza süresi 12 uçaklık filo için 60 günden 40 güne düşerek 20 ek operasyonel gün ve 1 milyon dolar ekstra gelir elde edilmesini sağlamaktadır. İşçilik maliyetleri de azalmış, uçak başına yıllık adam-saat 1586’dan 1153’e düşerek filo genelinde 5196 saat tasarruf sağlanmıştır. Bu optimizasyon, yıllık 416.000 dolarlık işgücü maliyeti tasarrufu ve toplamda 1416 milyon dolarlık mali fayda anlamına gelmektedir. Görevlerin A- ve L-bakımları arasında yeniden dağıtılması verimliliği daha da artırmıştır. Yağlama ve küçük denetimler gibi görevler birleştirilmiş ve yolcu deneyimini korumak için 1500 FH’deki kapsamlı kabin temizliği 500 FH’deki ara temizlik ile desteklenmiştir. Bu ayarlamalar, geri dönüş süresini veya güvenliği etkilemeden iş yükünü dengelemiştir. Bu optimizasyon, havacılık bakımında önemli maliyet tasarrufu ve operasyonel iyileştirme potansiyelini ortaya koymaktadır. A-bakım aralığının uzatılması filonun kullanılabilirliğini artırmış, işgücü gereksinimlerini azaltmış ve düzenleyici standartlara uygunluğu sağlamıştır. Bulgular, stratejik bakım planlamasının önemini ve diğer uçak tipleri ve filolarında da benzer optimizasyonların yapılma potansiyelini vurgulamaktadır.

Keywords

Optimizasyon havacılık uçak bakımı Taguchi metodu

References

  • Ab-Samat, H., & Kamaruddin, S. (2014). Opportunistic maintenance (OM) as a new advancement in maintenance approaches: A review. Journal of Quality in Maintenance Engineering, 20(2), 98-121.
  • Ahmadi, A., Söderholm, P., & Kumar, U. (2010). On aircraft scheduled maintenance program development. Journal of Quality in Maintenance Engineering, 16(3), 229-255.
  • Al-Thani, N. A., Ahmed, M. B., & Haouari, M. (2016). A model and optimization-based heuristic for the operational aircraft maintenance routing problem. Transportation Research Part C: Emerging Technologies, 72, 29-44.
  • Albakkoush, S., Pagone, E., & Salonitis, K. (2021). An approach to airline MRO operators planning and scheduling during aircraft line maintenance checks using discrete event simulation. Procedia Manufacturing, 54, 160-165.
  • Azadeh, A., Gharibdousti, M. S., Firoozi, M., Baseri, M., Alishahi, M., & Salehi, V. (2016). Selection of optimum maintenance policy using an integrated multi-criteria Taguchi modeling approach by considering resilience engineering. The International Journal of Advanced Manufacturing Technology, 84, 1067-1079.
  • Baptista, M., de Medeiros, I. P., Malere, J. P., Nascimento Jr, C., Prendinger, H., & Henriques, E. M. (2017). Comparative case study of life usage and data-driven prognostics techniques using aircraft fault messages. Computers in Industry, 86, 1-14.
  • Beliën, J., Cardoen, B., & Demeulemeester, E. (2012). Improving workforce scheduling of aircraft line maintenance at Sabena Technics. Interfaces, 42(4), 352-364.
  • Boeing. (2008). 777 family maintenance analysis & budget. Aircraft Commerce, 60, 12-23.
  • Boeing. (2013). Assessing the 777’s long-term base maintenance costs. Aircraft Commerce, 87, 28-42.
  • Boeing. (2016). Boeing 777-300ER Maintenance Interval. https://www.boeing-me.com/en/products-and-services/zzbuckit/777
  • Boeing. (2022). 777-200/-300 Airplane Characteristics for Airport Planning. https://www.boeing.com/content/dam/boeing/boeingdotcom/commercial/airports/acaps/777_2_2er_3.pdf
  • Bowers, M. R., Bichescu, B. C., Bryan, N., Polak, G. G., Gilbert, K., & Keene, D. (2022). The maintenance conversion scheduling problem: Models and insights. Naval Research Logistics, 69(7), 1027-1044.
  • Broderick, S. (2020). Emirates Customizes 777 Checks Using Own Data. https://aviationweek.com/air-transport/emirates-customizes-777-checks-using-own-data
  • Deng, Q., Santos, B. F., & Curran, R. (2020). A practical dynamic programming based methodology for aircraft maintenance check scheduling optimization. European Journal of Operational Research, 281(2), 256-273.
  • Deng, Q., Santos, B. F., & Verhagen, W. J. (2021). A novel decision support system for optimizing aircraft maintenance check schedule and task allocation. Decision Support Systems, 146, 113545.
  • Duvignau, R., Heinisch, V., Göransson, L., Gulisano, V., & Papatriantafilou, M. (2021). Benefits of small-size communities for continuous cost-optimization in peer-to-peer energy sharing. Applied Energy, 301, 117402.
  • Eltoukhy, A. E. E., Wang, Z. X., Chan, F. T. S., Chung, S. H., Ma, H. L., & Wang, X. P. (2020). Robust Aircraft Maintenance Routing Problem Using a Turn-Around Time Reduction Approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 4919-4932.
  • Esangbedo, M. O., & Samuel, B. O. (2024). Application of machine learning and grey Taguchi technique for the development and optimization of a natural fiber hybrid reinforced polymer composite for aircraft body manufacture. Oxford Open Materials Science, 4(1), itae004.
  • Fu, S., & Avdelidis, N. P. (2023). Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview. Sensors, 23(19), 8124.
  • Ghobbar, A. A. (2010). Aircraft Maintenance Engineering. Encyclopedia of Aerospace Engineering, John Wiley & Sons, Ltd, 1-13.
  • Jaafaru, H., & Agbelie, B. (2022). Bridge maintenance planning framework using machine learning, multi-attribute utility theory and evolutionary optimization models. Automation in Construction, 141, 104460.
  • Kabashkin, I., Perekrestov, V., Tyncherov, T., Shoshin, L., & Susanin, V. (2024). Framework for Integration of Health Monitoring Systems in Life Cycle Management for Aviation Sustainability and Cost Efficiency. Sustainability, 16(14), 6154.
  • Karaoğlu, U., Mbah, O., & Zeeshan, Q. (2023). Applications of machine learning in aircraft maintenance. Journal of Engineering Management and Systems Engineering, 2(1), 76-95.
  • Korba, P., Šváb, P., Vereš, M., & Lukáč, J. (2023). Optimizing aviation maintenance through algorithmic approach of real-life data. Applied Sciences, 13(6), 3824.
  • Kulkarni, A., Yadav, D. K., & Nikraz, H. (2017). Aircraft maintenance checks using critical chain project path. Aircraft Engineering and Aerospace Technology, 89(6), 879-892.
  • Lin, Z. L., Huang, Y. S., & Fang, C. C. (2015). Non-periodic preventive maintenance with reliability thresholds for complex repairable systems. Reliability Engineering & System Safety, 136, 145-156.
  • Martone, F., Zazzaro, G., Inverno, M., De Luca, S., Mainieri, F., & Romano, G. (2024). A Supporting Framework for Aircraft MRO Operations: Capacity Planning, Tasks Traceability and Disembarked Items Tracking. In 2024 34th Congress of the International Council of the Aeronautical Sciences (ICAS) (pp. 1-19).
  • Mattila, V., & Virtanen, K. (2014). Maintenance scheduling of a fleet of fighter aircraft through multi-objective simulation-optimization. Simulation, 90(9), 1023-1040.
  • Mofokeng, T. J., & Marnewick, A. (2017, June). Factors contributing to delays regarding aircraft during A-check maintenance. In 2017 IEEE Technology & Engineering Management Conference (TEMSCON) (pp. 185-190). IEEE.
  • Papakostas, N., Papachatzakis, P., Xanthakis, V., Mourtzis, D., & Chryssolouris, G. (2010). An approach to operational aircraft maintenance planning. Decision Support Systems, 48(4), 604-612.
  • Pimapunsri, K., & Weeranant, D. (2018). Solving complexity and resource-constrained project scheduling problem in aircraft heavy maintenance. International Journal of Applied Engineering Research, 13(11), 8998-9004.
  • Pop, G. I., Titu, A. M., & Pop, A. B. (2023). Enhancing Aerospace Industry Efficiency and Sustainability: Process Integration and Quality Management in the Context of Industry 4.0. Sustainability, 15(23), 16206.
  • Rao, M. V., Chaitanya, M. S. R. K., & Vidhu, K. P. (2017). Aircraft servicing, maintenance, repair & overhaul–the changed scenarios through outsourcing. International Journal of Research in Engineering and Applied Sciences, 7(5), 249-270.
  • Regattieri, A., Giazzi, A., Gamberi, M., & Gamberini, R. (2015). An innovative method to optimize the maintenance policies in an aircraft: General framework and case study. Journal of air transport management, 44, 8-20.
  • Saranga, H., & Kumar, U. D. (2006). Optimization of aircraft maintenance/support infrastructure using genetic algorithms—level of repair analysis. Annals of Operations Research, 143, 91-106.
  • Sarhani, M., Ezzinbi, O., El Afia, A., & Benadada, Y. (2016, May). Particle swarm optimization with a mutation operator for solving the preventive aircraft maintenance routing problem. In 2016 3rd International Conference on Logistics Operations Management (GOL) (pp. 1-6). IEEE.
  • Shandookh, A. A., Ogaili, A. A. F., & Al-Haddad, L. A. (2024). Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features. Heliyon, 10(13), e34202.
  • Shaukat, S., Katscher, M., Wu, C. L., Delgado, F., & Larrain, H. (2020). Aircraft line maintenance scheduling and optimisation. Journal of Air Transport Management, 89, 101914.
  • Sriram, C., & Haghani, A. (2003). An optimization model for aircraft maintenance scheduling and re-assignment. Transportation Research Part A: Policy and Practice, 37(1), 29-48.
  • Sukthomya, W., & Tannock, J. D. (2005). Taguchi experimental design for manufacturing process optimisation using historical data and a neural network process model. International Journal of Quality & Reliability Management, 22(5), 485-502.
  • Şentürk, C., Kavsaoğlu, M. S., & Nikbay, M. (2010, September). Optimization of aircraft utilization by reducing scheduled maintenance downtime. In 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference (p. 9143).
  • Tyagi, A., Tripathi, R., & Bouarfa, S. (2023). Learning from past in the aircraft maintenance industry: An empirical evaluation in the safety management framework. Heliyon, 9(11), e21620.
  • van der Weide, T., Deng, Q., & Santos, B. F. (2022). Robust long-term aircraft heavy maintenance check scheduling optimization under uncertainty. Computers & Operations Research, 141, 105667.
  • Verhagen, W. J., Santos, B. F., Freeman, F., van Kessel, P., Zarouchas, D., Loutas, T., ... & Heiets, I. (2023). Condition-Based Maintenance in Aviation: Challenges and Opportunities. Aerospace, 10(9), 762.
  • Vieira, D. R., & Loures, P. L. (2016). Maintenance, repair and overhaul (MRO) fundamentals and strategies: An aeronautical industry overview. International Journal of Computer Applications, 135(12), 21-29.
  • Yang, Z., & Yang, G. (2012). Optimization of Aircraft Maintenance plan based on Genetic Algorithm. Physics Procedia, 33, 580-586.
  • Yilmaz, O., Gindy, N., & Gao, J. (2010). A repair and overhaul methodology for aeroengine components. Robotics and Computer-Integrated Manufacturing, 26(2), 190-201.
  • Zhang, Q., Chung, S. H., Ma, H. L., & Sun, X. (2024). Robust aircraft maintenance routing with heterogenous aircraft maintenance tasks. Transportation Research Part C: Emerging Technologies, 160, 104518.
  • Zio, E., Fan, M., Zeng, Z., & Kang, R. (2019). Application of reliability technologies in civil aviation: Lessons learnt and perspectives. Chinese Journal of Aeronautics, 32(1), 143-158.

TAGUCHI METHOD-BASED MAN-HOUR OPTIMIZATION FOR BOEING 777 A-CHECK INTERVAL

Year 2025, Volume: 24 Issue: 47, 201 - 216, 30.06.2025

Cahit Bilgi , Can Eyüpoğlu , Abdullah Bülbül

https://doi.org/10.55071/ticaretfbd.1633385

Abstract

Aircraft maintenance is critical for safety and operational efficiency. For the Boeing 777, A-checks are conducted every 1000 flight hours (FH), resulting in five checks per aircraft annually, with significant downtime and labor costs. This study proposes a Taguchi method-based man-hour optimization model extending the A-check interval to 1500 FH and reducing the number of annual checks while maintaining safety and regulatory compliance. The analysis carried out in this study revealed significant benefits of this interval extension. Annual downtime for the 12-aircraft fleet decreased from 60 to 40 days, allowing 20 additional operational days and generating $1 million in extra revenue. Labor costs were also reduced, with annual man-hours dropping from 1586 to 1153 per aircraft, saving 5196 hours fleet-wide. This optimization translates to $416.000 in labor cost savings annually, with a total financial benefit of $1416 million. Redistributing tasks between A- and L-checks further enhanced efficiency. Tasks such as lubrication and minor inspections were consolidated, and comprehensive cabin cleaning at 1500 FH was supplemented with intermediate cleaning at 500 FH to maintain passenger experience. These adjustments balanced the workload without affecting turnaround time or safety. This optimization demonstrates the potential for significant cost savings and operational improvements in aviation maintenance. Extending the A-check interval increased fleet availability, reduced labor requirements, and ensured compliance with regulatory standards. The findings highlight the importance of strategic maintenance planning and the potential for similar optimizations across other aircraft types and fleets.

Keywords

Optimization aviation aircraft maintenance Taguchi method

References

  • Ab-Samat, H., & Kamaruddin, S. (2014). Opportunistic maintenance (OM) as a new advancement in maintenance approaches: A review. Journal of Quality in Maintenance Engineering, 20(2), 98-121.
  • Ahmadi, A., Söderholm, P., & Kumar, U. (2010). On aircraft scheduled maintenance program development. Journal of Quality in Maintenance Engineering, 16(3), 229-255.
  • Al-Thani, N. A., Ahmed, M. B., & Haouari, M. (2016). A model and optimization-based heuristic for the operational aircraft maintenance routing problem. Transportation Research Part C: Emerging Technologies, 72, 29-44.
  • Albakkoush, S., Pagone, E., & Salonitis, K. (2021). An approach to airline MRO operators planning and scheduling during aircraft line maintenance checks using discrete event simulation. Procedia Manufacturing, 54, 160-165.
  • Azadeh, A., Gharibdousti, M. S., Firoozi, M., Baseri, M., Alishahi, M., & Salehi, V. (2016). Selection of optimum maintenance policy using an integrated multi-criteria Taguchi modeling approach by considering resilience engineering. The International Journal of Advanced Manufacturing Technology, 84, 1067-1079.
  • Baptista, M., de Medeiros, I. P., Malere, J. P., Nascimento Jr, C., Prendinger, H., & Henriques, E. M. (2017). Comparative case study of life usage and data-driven prognostics techniques using aircraft fault messages. Computers in Industry, 86, 1-14.
  • Beliën, J., Cardoen, B., & Demeulemeester, E. (2012). Improving workforce scheduling of aircraft line maintenance at Sabena Technics. Interfaces, 42(4), 352-364.
  • Boeing. (2008). 777 family maintenance analysis & budget. Aircraft Commerce, 60, 12-23.
  • Boeing. (2013). Assessing the 777’s long-term base maintenance costs. Aircraft Commerce, 87, 28-42.
  • Boeing. (2016). Boeing 777-300ER Maintenance Interval. https://www.boeing-me.com/en/products-and-services/zzbuckit/777
  • Boeing. (2022). 777-200/-300 Airplane Characteristics for Airport Planning. https://www.boeing.com/content/dam/boeing/boeingdotcom/commercial/airports/acaps/777_2_2er_3.pdf
  • Bowers, M. R., Bichescu, B. C., Bryan, N., Polak, G. G., Gilbert, K., & Keene, D. (2022). The maintenance conversion scheduling problem: Models and insights. Naval Research Logistics, 69(7), 1027-1044.
  • Broderick, S. (2020). Emirates Customizes 777 Checks Using Own Data. https://aviationweek.com/air-transport/emirates-customizes-777-checks-using-own-data
  • Deng, Q., Santos, B. F., & Curran, R. (2020). A practical dynamic programming based methodology for aircraft maintenance check scheduling optimization. European Journal of Operational Research, 281(2), 256-273.
  • Deng, Q., Santos, B. F., & Verhagen, W. J. (2021). A novel decision support system for optimizing aircraft maintenance check schedule and task allocation. Decision Support Systems, 146, 113545.
  • Duvignau, R., Heinisch, V., Göransson, L., Gulisano, V., & Papatriantafilou, M. (2021). Benefits of small-size communities for continuous cost-optimization in peer-to-peer energy sharing. Applied Energy, 301, 117402.
  • Eltoukhy, A. E. E., Wang, Z. X., Chan, F. T. S., Chung, S. H., Ma, H. L., & Wang, X. P. (2020). Robust Aircraft Maintenance Routing Problem Using a Turn-Around Time Reduction Approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 4919-4932.
  • Esangbedo, M. O., & Samuel, B. O. (2024). Application of machine learning and grey Taguchi technique for the development and optimization of a natural fiber hybrid reinforced polymer composite for aircraft body manufacture. Oxford Open Materials Science, 4(1), itae004.
  • Fu, S., & Avdelidis, N. P. (2023). Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview. Sensors, 23(19), 8124.
  • Ghobbar, A. A. (2010). Aircraft Maintenance Engineering. Encyclopedia of Aerospace Engineering, John Wiley & Sons, Ltd, 1-13.
  • Jaafaru, H., & Agbelie, B. (2022). Bridge maintenance planning framework using machine learning, multi-attribute utility theory and evolutionary optimization models. Automation in Construction, 141, 104460.
  • Kabashkin, I., Perekrestov, V., Tyncherov, T., Shoshin, L., & Susanin, V. (2024). Framework for Integration of Health Monitoring Systems in Life Cycle Management for Aviation Sustainability and Cost Efficiency. Sustainability, 16(14), 6154.
  • Karaoğlu, U., Mbah, O., & Zeeshan, Q. (2023). Applications of machine learning in aircraft maintenance. Journal of Engineering Management and Systems Engineering, 2(1), 76-95.
  • Korba, P., Šváb, P., Vereš, M., & Lukáč, J. (2023). Optimizing aviation maintenance through algorithmic approach of real-life data. Applied Sciences, 13(6), 3824.
  • Kulkarni, A., Yadav, D. K., & Nikraz, H. (2017). Aircraft maintenance checks using critical chain project path. Aircraft Engineering and Aerospace Technology, 89(6), 879-892.
  • Lin, Z. L., Huang, Y. S., & Fang, C. C. (2015). Non-periodic preventive maintenance with reliability thresholds for complex repairable systems. Reliability Engineering & System Safety, 136, 145-156.
  • Martone, F., Zazzaro, G., Inverno, M., De Luca, S., Mainieri, F., & Romano, G. (2024). A Supporting Framework for Aircraft MRO Operations: Capacity Planning, Tasks Traceability and Disembarked Items Tracking. In 2024 34th Congress of the International Council of the Aeronautical Sciences (ICAS) (pp. 1-19).
  • Mattila, V., & Virtanen, K. (2014). Maintenance scheduling of a fleet of fighter aircraft through multi-objective simulation-optimization. Simulation, 90(9), 1023-1040.
  • Mofokeng, T. J., & Marnewick, A. (2017, June). Factors contributing to delays regarding aircraft during A-check maintenance. In 2017 IEEE Technology & Engineering Management Conference (TEMSCON) (pp. 185-190). IEEE.
  • Papakostas, N., Papachatzakis, P., Xanthakis, V., Mourtzis, D., & Chryssolouris, G. (2010). An approach to operational aircraft maintenance planning. Decision Support Systems, 48(4), 604-612.
  • Pimapunsri, K., & Weeranant, D. (2018). Solving complexity and resource-constrained project scheduling problem in aircraft heavy maintenance. International Journal of Applied Engineering Research, 13(11), 8998-9004.
  • Pop, G. I., Titu, A. M., & Pop, A. B. (2023). Enhancing Aerospace Industry Efficiency and Sustainability: Process Integration and Quality Management in the Context of Industry 4.0. Sustainability, 15(23), 16206.
  • Rao, M. V., Chaitanya, M. S. R. K., & Vidhu, K. P. (2017). Aircraft servicing, maintenance, repair & overhaul–the changed scenarios through outsourcing. International Journal of Research in Engineering and Applied Sciences, 7(5), 249-270.
  • Regattieri, A., Giazzi, A., Gamberi, M., & Gamberini, R. (2015). An innovative method to optimize the maintenance policies in an aircraft: General framework and case study. Journal of air transport management, 44, 8-20.
  • Saranga, H., & Kumar, U. D. (2006). Optimization of aircraft maintenance/support infrastructure using genetic algorithms—level of repair analysis. Annals of Operations Research, 143, 91-106.
  • Sarhani, M., Ezzinbi, O., El Afia, A., & Benadada, Y. (2016, May). Particle swarm optimization with a mutation operator for solving the preventive aircraft maintenance routing problem. In 2016 3rd International Conference on Logistics Operations Management (GOL) (pp. 1-6). IEEE.
  • Shandookh, A. A., Ogaili, A. A. F., & Al-Haddad, L. A. (2024). Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features. Heliyon, 10(13), e34202.
  • Shaukat, S., Katscher, M., Wu, C. L., Delgado, F., & Larrain, H. (2020). Aircraft line maintenance scheduling and optimisation. Journal of Air Transport Management, 89, 101914.
  • Sriram, C., & Haghani, A. (2003). An optimization model for aircraft maintenance scheduling and re-assignment. Transportation Research Part A: Policy and Practice, 37(1), 29-48.
  • Sukthomya, W., & Tannock, J. D. (2005). Taguchi experimental design for manufacturing process optimisation using historical data and a neural network process model. International Journal of Quality & Reliability Management, 22(5), 485-502.
  • Şentürk, C., Kavsaoğlu, M. S., & Nikbay, M. (2010, September). Optimization of aircraft utilization by reducing scheduled maintenance downtime. In 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference (p. 9143).
  • Tyagi, A., Tripathi, R., & Bouarfa, S. (2023). Learning from past in the aircraft maintenance industry: An empirical evaluation in the safety management framework. Heliyon, 9(11), e21620.
  • van der Weide, T., Deng, Q., & Santos, B. F. (2022). Robust long-term aircraft heavy maintenance check scheduling optimization under uncertainty. Computers & Operations Research, 141, 105667.
  • Verhagen, W. J., Santos, B. F., Freeman, F., van Kessel, P., Zarouchas, D., Loutas, T., ... & Heiets, I. (2023). Condition-Based Maintenance in Aviation: Challenges and Opportunities. Aerospace, 10(9), 762.
  • Vieira, D. R., & Loures, P. L. (2016). Maintenance, repair and overhaul (MRO) fundamentals and strategies: An aeronautical industry overview. International Journal of Computer Applications, 135(12), 21-29.
  • Yang, Z., & Yang, G. (2012). Optimization of Aircraft Maintenance plan based on Genetic Algorithm. Physics Procedia, 33, 580-586.
  • Yilmaz, O., Gindy, N., & Gao, J. (2010). A repair and overhaul methodology for aeroengine components. Robotics and Computer-Integrated Manufacturing, 26(2), 190-201.
  • Zhang, Q., Chung, S. H., Ma, H. L., & Sun, X. (2024). Robust aircraft maintenance routing with heterogenous aircraft maintenance tasks. Transportation Research Part C: Emerging Technologies, 160, 104518.
  • Zio, E., Fan, M., Zeng, Z., & Kang, R. (2019). Application of reliability technologies in civil aviation: Lessons learnt and perspectives. Chinese Journal of Aeronautics, 32(1), 143-158.
There are 49 citations in total.

Details

Primary Language English
Subjects Business Process Management, Information Systems (Other), Optimization in Manufacturing
Journal Section Research Article
Authors

Cahit Bilgi 0000-0002-7432-2817

Can Eyüpoğlu 0000-0002-6133-8617

Abdullah Bülbül 0000-0002-6340-0998

Early Pub Date June 14, 2025
Publication Date June 30, 2025
Submission Date February 4, 2025
Acceptance Date April 15, 2025
Published in Issue Year 2025 Volume: 24 Issue: 47

Cite

APA Bilgi, C., Eyüpoğlu, C., & Bülbül, A. (2025). TAGUCHI METHOD-BASED MAN-HOUR OPTIMIZATION FOR BOEING 777 A-CHECK INTERVAL. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 24(47), 201-216. https://doi.org/10.55071/ticaretfbd.1633385
AMA Bilgi C, Eyüpoğlu C, Bülbül A. TAGUCHI METHOD-BASED MAN-HOUR OPTIMIZATION FOR BOEING 777 A-CHECK INTERVAL. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi. June 2025;24(47):201-216. doi:10.55071/ticaretfbd.1633385
Chicago Bilgi, Cahit, Can Eyüpoğlu, and Abdullah Bülbül. “TAGUCHI METHOD-BASED MAN-HOUR OPTIMIZATION FOR BOEING 777 A-CHECK INTERVAL”. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 24, no. 47 (June 2025): 201-16. https://doi.org/10.55071/ticaretfbd.1633385.
EndNote Bilgi C, Eyüpoğlu C, Bülbül A (June 1, 2025) TAGUCHI METHOD-BASED MAN-HOUR OPTIMIZATION FOR BOEING 777 A-CHECK INTERVAL. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 24 47 201–216.
IEEE C. Bilgi, C. Eyüpoğlu, and A. Bülbül, “TAGUCHI METHOD-BASED MAN-HOUR OPTIMIZATION FOR BOEING 777 A-CHECK INTERVAL”, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, vol. 24, no. 47, pp. 201–216, 2025, doi: 10.55071/ticaretfbd.1633385.
ISNAD Bilgi, Cahit et al. “TAGUCHI METHOD-BASED MAN-HOUR OPTIMIZATION FOR BOEING 777 A-CHECK INTERVAL”. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 24/47 (June 2025), 201-216. https://doi.org/10.55071/ticaretfbd.1633385.
JAMA Bilgi C, Eyüpoğlu C, Bülbül A. TAGUCHI METHOD-BASED MAN-HOUR OPTIMIZATION FOR BOEING 777 A-CHECK INTERVAL. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi. 2025;24:201–216.
MLA Bilgi, Cahit et al. “TAGUCHI METHOD-BASED MAN-HOUR OPTIMIZATION FOR BOEING 777 A-CHECK INTERVAL”. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, vol. 24, no. 47, 2025, pp. 201-16, doi:10.55071/ticaretfbd.1633385.
Vancouver Bilgi C, Eyüpoğlu C, Bülbül A. TAGUCHI METHOD-BASED MAN-HOUR OPTIMIZATION FOR BOEING 777 A-CHECK INTERVAL. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi. 2025;24(47):201-16.
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