Randomized-Variants Lower Bounds for Gas Turbines Aircraft Engines

  • Mahdi JemmaliEmail author
  • Loai Kayed B. Melhim
  • Mafawez Alharbi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


This paper focuses on the problem of developing a heuristic model for identical aircraft gas turbine engines maintenance interventions. Each turbine has some parts which requires replacement (changing the used part by a new one) at well determined periods. The maintenance problem of identical aircraft gas turbine engines will be addressed in this research, in order to maximize crafts operation time without affecting engine maintenance schedule. Maintaining the turbine is performed by using new or refurbished parts, at specific predetermined periods. These parts (new or refurbished) have a predetermined lifespan; this research discusses how to replace a sequence of turbine parts in the turbine maintenance process, in order to maximizing aircraft operating time. In this research, 4 heuristics were developed to achieve the proposed goal. analyzing the obtained results showed that heuristic \(R_4\) obtained the best \(Tu_{min}\) value.


Heuristic Scheduling Randomization algorithms Parallel machines 



The author would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work.


  1. 1.
    Edmunds, D.B.: Modular engine maintenance concept considerations for aircraft turbine engines. Aircr. Eng. Aerosp. Technol. 50(1), 14–17 (1978)Google Scholar
  2. 2.
    Gharbi, A.: Scheduling maintenance actions for gas turbines aircraft engines. Constraints 10, 4 (2014)Google Scholar
  3. 3.
    Haouari, M., Jemmali, M.: Maximizing the minimum completion time on parallel machines. 4OR 6(4), 375–392 (2008)Google Scholar
  4. 4.
    Lawler, E.L., Lenstra, J.K., Kan, A.H.R., Shmoys, D.B.: Sequencing and scheduling: algorithms and complexity. Handb. Oper. Res. Manag. Sci. 4, 445–522 (1993)Google Scholar
  5. 5.
    Mokotoff, E.: Parallel machine scheduling problems: a survey. Asia-Pac. J. Oper. Res. 18(2), 193 (2001)Google Scholar
  6. 6.
    Tan, Z., He, Y., Epstein, L.: Optimal on-line algorithms for the uniform machine scheduling problem with ordinal data. Inf. Comput. 196(1), 57–70 (2005)Google Scholar
  7. 7.
    Walter, R., Lawrinenko, A.: Effective solution space limitation for the identical parallel machine scheduling problem. Technical report, Working Paper (2014).
  8. 8.
    Walter, R., Wirth, M., Lawrinenko, A.: Improved approaches to the exact solution of the machine covering problem. J. Sched. 20(2), 147–164 (2017)Google Scholar
  9. 9.
    Woeginger, G.J.: A polynomial-time approximation scheme for maximizing the minimum machine completion time. Oper. Res. Lett. 20(4), 149–154 (1997)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and Information, College of ScienceMajmaah UniversityAl-MajmaahSaudi Arabia

Personalised recommendations