Research Papers On Aircraft Maintenance Management

  • Adida, E., Joshi, P.: A robust optimisation approach to project scheduling and resource allocation. Int. J. Serv. Oper. Informatics 4, 169–193 (2009). doi:10.1504/IJSOI.2009.023421Google Scholar

  • Ahire, S., Greenwood, G., Gupta, A., Terwilliger, M.: Workforce-constrained Preventive Maintenance Scheduling Using Evolution Strategies. Decis. Sci. 31, 833–859 (2000). doi:10.1111/j.1540-5915.2000.tb00945.xGoogle Scholar

  • Atasoy, B., Güllü, R., Tan, T.: Optimal inventory policies with non-stationary supply disruptions and advance supply information. Decis. Support Syst. 53, 269–281 (2012). doi:10.1016/j.dss.2012.01.005Google Scholar

  • Bertsimas, D., Gupta, S., Lulli, G.: Dynamic resource allocation: A flexible and tractable modeling framework. Eur. J. Oper. Res. 236, 14–26 (2014). doi:10.1016/j.ejor.2013.10.063Google Scholar

  • Budai, G., Dekker, R., Nicolai, R.P.: Maintenance and production: A review of planning models. In: Kobbacy, K.A.H., Murthy, D.N.P. (eds.) Complex Syst. Maint. Handb., pp. 321–344. Springer, London (2008)CrossRefGoogle Scholar

  • CEN EN 13306:2001 - Maintenance Terminology. European Standard. European Committee for Standardization, Brussels (2001)Google Scholar

  • Dijkstra, M.C., Kroon, L.G., Salomon, M., et al.: Planning the Size and Organization of KLM’s Aircraft Maintenance Personnel. Interfaces (Providence) 24, 47–58 (1994). doi:10.1287/inte.24.6.47Google Scholar

  • Dijkstra, M.C., Kroon, L.G., van Nunen, J.A.E.E., Salomon, M.: A DSS for capacity planning of aircraft maintenance personnel. Int. J. Prod. Econ. 23, 69–78 (1991). doi:10.1016/0925-5273(91)90049-YGoogle Scholar

  • Fogel, D.B.: Phenotypes, genotypes, and operators in evolutionary computation. In: Proc. 1995 IEEE Int. Conf. Evol. Comput. (1995). doi: 10.1109/ICEC.1995.489143Google Scholar

  • Fortuin, L., Martin, H.: Control of service parts. Int. J. Oper. Prod. Manag. 19, 950–971 (1999). doi:10.1108/01443579910280287Google Scholar

  • Ghobbar, A.A., Friend, C.H.: Sources of intermittent demand for aircraft spare parts within airline operations. J. Air. Transp. Manag. 8, 221–231 (2002). doi:10.1016/S0969-6997(01)00054-0Google Scholar

  • Ghobbar, A.A., Friend, C.H.: Evaluation of forecasting methods for intermittent parts demand in the field of: a predictive model. Comput. Oper. Res. 30, 2097–2114 (2003)CrossRefMATHGoogle Scholar

  • Gu, J., Zhang, G., Li, K.W.: Efficient aircraft spare parts inventory management under demand uncertainty. J. Air. Transp. Manag. 42, 101–109 (2015). doi:10.1016/j.jairtraman.2014.09.006Google Scholar

  • Gutierrez, R.S., Solis, A.O., Mukhopadhyay, S.: Lumpy demand forecasting using neural networks. Int. J. Prod. Econ. 111, 409–420 (2008). doi:10.1016/j.ijpe.2007.01.007Google Scholar

  • Huang, Z., Chang, W., Xiao, Y., Liu, R.: Optimizing human resources allocation on aircraft maintenance with predefined sequence. In: 2010 Int. Conf. Logist. Syst. Intell. Manag., ICLSIM 2010, vol. 2, pp. 1018–1022 (2010). doi:10.1109/ICLSIM.2010.5461109Google Scholar

  • Ighravwe, D.E., Oke, S.A.: A non-zero integer non-linear programming model for maintenance workforce sizing. Int. J. Prod. Econ. 150, 204–214 (2014). doi:10.1016/j.ijpe.2014.01.004Google Scholar

  • Karimi, H., Rahmati, S.H.A., Zandieh, M.: An efficient knowledge-based algorithm for the flexible job shop scheduling problem. Knowledge-Based Syst. 36, 236–244 (2012). doi:10.1016/j.knosys.2012.04.001Google Scholar

  • Kobbacy, K.A.H., Murthy, D.N.P.: An overview. In: Kobbacy, K.A.H., Murthy, D.N.P. (eds.) Complex Syst. Maint. Handb., pp. 3–18. Springer, London (2008)CrossRefGoogle Scholar

  • Kourentzes, N.: Intermittent demand forecasts with neural networks. Int. J. Prod. Econ. 143, 198–206 (2013). doi:10.1016/j.ijpe.2013.01.009Google Scholar

  • Marquez, A.C., Gupta, J.N.D.: Contemporary maintenance management: process, framework and supporting pillars. Omega 34, 313–326 (2006). doi:10.1016/j.omega.2004.11.003Google Scholar

  • Meeran, S., Morshed, M.S.: A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study. J. Intell. Manuf. 23, 1063–1078 (2012). doi:10.1007/s10845-011-0520-xGoogle Scholar

  • Mobley, R.K.: Maintenance Fundamentals, 2nd edn. Butterworth-Heinemann (2004)Google Scholar

  • Pintelon, L., Parodi-Herz, A.: Maintenance: An evolutionary perspective. In: Kobbacy, K.A.H., Murthy, D.N.P. (eds.) Complex Syst. Maint. Handb., pp. 21–48. Springer, London (2008)CrossRefGoogle Scholar

  • Rajkumar, M., Asokan, P., Anilkumar, N., Page, T.: A GRASP algorithm for flexible job-shop scheduling problem with limited resource constraints. Int. J. Prod. Res. 49, 2409–2423 (2011). doi:10.1080/00207541003709544Google Scholar

  • Regattieri, A., Gamberi, M., Gamberini, R., Manzini, R.: Managing lumpy demand for aircraft spare parts. J. Air Transp. Manag. 11, 426–431 (2005). doi:10.1016/j.jairtraman.2005.06.003Google Scholar

  • Romeijnders, W., Teunter, R., Van Jaarsveld, W.: A two-step method for forecasting spare parts demand using information on component repairs. Eur. J. Oper. Res. 220, 386–393 (2012). doi:10.1016/j.ejor.2012.01.019Google Scholar

  • Safaei, N., Banjevic, D., Jardine, A.K.S.: Workforce-constrained maintenance scheduling for military aircraft fleet: a case study. Ann. Oper. Res. 186, 295–316 (2011). doi:10.1007/s10479-011-0885-4Google Scholar

  • Samaranayake, P., Kiridena, S.: Aircraft maintenance planning and scheduling: an integrated framework. J. Qual. Maint. Eng. 18, 432–453 (2012). doi:10.1108/13552511211281598Google Scholar

  • Thörnblad, K., Almgren, T., Patriksson, M., Strömberg, A.-B.: Mathematical optimization of a flexible job shop problem including preventive maintenance and availability of fixtures. In: Proc. 4th World P&OM Conf. / 19th Int. Annu. EurOMA Conf., Amsterdam, Netherlands, pp. 1–10, July 2012Google Scholar

  • Wang, W.: A stochastic model for joint spare parts inventory and planned maintenance optimisation. Eur. J. Oper. Res. 216, 127–139 (2012). doi:10.1016/j.ejor.2011.07.031Google Scholar

  • Weckman, G., Bondal, A.A., Rinder, M.M., Young, W.A.: Applying a hybrid artificial immune systems to the job shop scheduling problem. Neural Comput. Appl. 21, 1465–1475 (2012). doi:10.1007/s00521-012-0852-2Google Scholar

  • Yan, S., Yang, T.H., Chen, H.H.: Airline short-term maintenance manpower supply planning. Transp. Res. Part A Policy Pract. 38, 615–642 (2004). doi:10.1016/j.tra.2004.03.005Google Scholar

  • Yang, H., Sun, Q., Saygin, C., Sun, S.: Job shop scheduling based on earliness and tardiness penalties with due dates and deadlines: an enhanced genetic algorithm. Int. J. Adv. Manuf. Technol. pp. 657–666 (2012). doi: 10.1007/s00170-011-3746-zGoogle Scholar

  • Yang, T.H., Yan, S., Chen, H.H.: An airline maintenance manpower planning model with flexible strategies. J. Air Transp. Manag. 9, 233–239 (2003). doi:10.1016/S0969-6997(03)00013-9Google Scholar

  • Zanjani, M.K., Nourelfath, M.: Integrated spare parts logistics and operations planning for maintenance service providers. Int. J. Prod. Econ. 158, 44–53 (2014). doi:10.1016/j.ijpe.2014.07.012Google Scholar

  • Technology is the reflection of science, which is developing in every field at any moment. Developments in physics, chemistry, biology and countless sciences allow the development of an existing product (technology) or the production of a... more

    Technology is the reflection of science, which is developing in every field at any moment. Developments in physics, chemistry, biology and countless sciences allow the development of an existing product (technology) or the production of a different one. Undoubtedly, advanced technology is not the product of a single science. Science progresses with partner stakeholders and collaborations. As science uncovers the universe and reveals the secrets of nature, mankind will continue to advance. Development does not pause. Sometimes nature takes a step to guide our thousand steps. Apple falls to Newton and explores gravity. But science does not stop, questions, inquires and explores. Newton discovered Gravitation. We learned that there is a gravitational force between two bodies, that this gravitational force is universal and rightly multiplied by the masses of the bodies, inversely proportional to the height of the distance between them. We have made gravitational force a gravitational force that everything in the world draws each other, that gravity is one of these forces, and that it is the greatest, and that we fall down when we throw down a body from a place, that everything in the world has a weight. While the science front looked as if the gates of the imaginary world were spaced apart, it took its place in the head of mankind. And it will continue. From Galilei and Kepler, Newton's Philosophiae Naturalis Principia Mathematica (Mathematical Principles of the Nature Philosophy) written in 1687, the discovery of Neptune in 1846, and the prediction of the existence of gravitational waves by Einstein in 1946, 100 years later, the signals from the Laser Interferometer Gravitational Wave Observatory (LIGO) scientists in Louisiana, which have a small vibration of gravitational waves generated by the collision of two giant black holes at a distance of 1.3 billion light years at a size 30 times the size of the sun, technology has been proven by the detection of two detectors by their newcomers. What did the engineers who chased the science do? What do we think, what have we developed, what have we produced? We always needed the gentle. From the animals, we used the muscle power. In 1760, Thomas Newcomen and James Watt found steam engines. We have produced internal combustion engines that are useful in chemistry. Carnot, Rankine, Nikolaus Otto, Ralph Miller, Carl Benz, Frank and Charles Duryea, Rudolf Diesel, Ferix Wankel, John Ericsson, Brayton. All and more. Scientists, inventors, who take steps in knowledge, technology, and put bricks together.

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