Annals of Operations Research

, Volume 180, Issue 1, pp 213–231 | Cite as

Optimisation of maintenance scheduling strategies on the grid

  • Alex Shenfield
  • Peter J. Fleming
  • Visakan Kadirkamanathan
  • Jeff Allan
Article

Abstract

The emerging paradigm of Grid Computing provides a powerful platform for the optimisation of complex computer models, such as those used to simulate real-world logistics and supply chain operations. This paper introduces a Grid-based optimisation framework that provides a powerful tool for the optimisation of such computationally intensive objective functions. This framework is then used in the optimisation of maintenance scheduling strategies for fleets of aero-engines, a computationally intensive problem with a high-degree of stochastic noise, achieving substantial improvements in the execution time of the algorithm.

Keywords

Maintenance scheduling Evolutionary optimisation Grid computing 

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References

  1. Abdalhaq, B., Cortes, A., Margalef, T., & Luque, E. (2002). Evolutionary optimization techniques on computational grids. In Proceedings of the international conference on computer science (ICCS2002) (pp. 513–522). Berlin: Springer. Google Scholar
  2. Argyle, J. P. M. (2006). Optimisation of operational cost with application to an aerospace engine system. PhD thesis, University of Sheffield. Google Scholar
  3. Argyle, J. P. M., & Tubby, J. (2002) Integrated logistics support optimisation (Technical Report RRUTC/Shef/R/02006). Rolls-Royce PLC. Google Scholar
  4. Axelrod, R. (1987). The evolution of strategies in the iterated prisoner’s dilemma. In L. Davis (Ed.), Genetic algorithms and simulated annealing (pp. 32–41). San Mateo: Morgan Kaufmann. Google Scholar
  5. Baker, J. E. (1987). Reducing bias and inefficiency in the selection algorithm. In J. J. Grefenstette (Ed.), Proceedings of the second international conference on genetic algorithms (pp. 14–21). Hillsdale: Erlbaum. Google Scholar
  6. Baker, M., Buyya, R., & Laforenza, D. (2002). Grid and grid technologies for wide area distributed computing. Software: Practice and Experience, 32(15), 1437–1466. CrossRefGoogle Scholar
  7. Berman, F., Wolski, R., Figueira, S., Schopf, J., & Shao, G. (1996) Application-level scheduling on distributed heterogeneous networks. In: Supercomputing ’96. Google Scholar
  8. Cantú-Paz, E., & Goldberg, D. E. (1999). On the scalability of parallel genetic algorithms. Evolutionary Computation, 7(4), 429–449. CrossRefGoogle Scholar
  9. Chappell, D. A., & Jewell, T. (2002). Java web services. Sebastopol: O’Reilly. Google Scholar
  10. Chipperfield, A. J., & Fleming, P. J. (1995). Parallel genetic algorithms. In A. Y. Zomaya (Ed.), Parallel and distributed computing handbook (pp. 1118–1144). New York: McGraw-Hill. Chap. 39. Google Scholar
  11. Crocker, J., & Kumar, U. D. (2000). Age-related maintenance versus reliability centred maintenance: A case study on aero-engines. Reliability Engineering and Systems Safety, 67, 113–118. CrossRefGoogle Scholar
  12. Davis, L. (1985). Job shop scheduling with genetic algorithms. In J. J. Grefenstette (Ed.), Proceedings of the first international conference on genetic algorithms (pp. 136–140). Hillsdale: Erlbaum. Google Scholar
  13. Distributed Systems Architecture Group, Universidad Complutense de Madrid. (2007). GridWay Metascheduler. http://www.gridway.org/index.php. Accessed 22nd April 2007.
  14. Fernandez, F., Tomassini, M., & Vanneschi, L. (2003). An empirical study of multipopulation genetic programming. Genetic Programming and Evolvable Machines, 4, 21–51. CrossRefGoogle Scholar
  15. Fleming, P. J., Purshouse, R. C., Chipperfield, A. J., Griffin, I. A., & Thompson, H. A. (2002). Control systems design with multiple objectives: An evolutionary computing approach. In: Workshops of the 15th IFAC world congress. Google Scholar
  16. Fogarty, T. C., & Huang, R. (1991). Implementing the genetic algorithm on transputer based parallel processing systems. In H. P. Schwefel & R. Männer (Eds.), Lecture notes in computer science : Vol. 496. Parallel problem solving from nature 1 (pp. 145–149). Berlin: Springer. CrossRefGoogle Scholar
  17. Fogel, D. B., & Ghoziel, A. (1997). A note on representations and variation operators. IEEE Transactions on Evolutionary Computation, 1(2), 159–161. CrossRefGoogle Scholar
  18. Foster, I., & Kesselman, C. (1999). The Globus Toolkit. In I. Foster & C. Kesselman (Eds.), The GRID: blueprint for a new computing infrastructure (pp. 259–278). San Mateo: Morgan Kaufmann. Chap. 11. Google Scholar
  19. Foster, I., Kesselman, C., & Tuecke, S. (2001). The anatomy of the grid: Enabling scalable virtual organizations. International Journal of Supercomputer Applications, 15(3), 200–222. CrossRefGoogle Scholar
  20. Foster, I., Kesselman, C., Nick, J. M., & Tuecke, S. (2002). Grid services for distributed system integration. IEEE Computer, 35(6), 37–46. Google Scholar
  21. Fung, C. C., Li, J. B., Wong, K. W., & Wang, K. P. (2004). A java-based parallel platform for the implementation of evolutionary computation for engineering applications. International Journal of Systems Science, 35(13–14), 741–750. CrossRefGoogle Scholar
  22. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading: Addison-Wesley. Google Scholar
  23. Grosso, P. B. (1985). Computer simulation of genetic adaptation: Parallel subcomponent interaction in a multilocus model. PhD thesis, University of Michigan. Google Scholar
  24. Hamby, D. M. (1994). A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring and Assessment, 32, 135–154. CrossRefGoogle Scholar
  25. Hancock, P. J. B. (1994). An empirical comparison of selection methods in evolutionary algorithms. In T. C. Fogarty (Ed.), Lecture notes in computer science : Vol. 865. Evolutionary computing—AISB workshop (pp. 80–94). Berlin: Springer. Google Scholar
  26. Herrera, J., Huedo, E., Montero, R. S., & Llorente, I. M. (2005). A grid-oriented genetic algorithm. In P. M. A. Sloot, A. G. Hoekstra, T. Priol, A. Reinefeld, & M. Bubak (Eds.), Lecture notes in computer science : Vol. 3470. Advances in grid computing: EGC 2005 (pp. 315–322). Berlin: Springer. CrossRefGoogle Scholar
  27. Jensen, M. T. (2003). Generating robust and flexible job shop schedules using genetic algorithms. IEEE Transactions on Evolutionary Computation, 7(3), 275–288. CrossRefGoogle Scholar
  28. Kleinrock, L. (1969). UCLA press release. http://www.lk.cs.ucla.edu/LK/Bib/REPORT/press.html.
  29. Kleinrock, L. (1975). Queueing systems, vol. 1: Theory. New York: Wiley. Google Scholar
  30. Langdon, W. B. (1995). Scheduling planned maintenance of the national grid. In T. C. Fogarty (Ed.), Lecture notes in computer science : Vol. 993. Evolutionary computing—AISB workshop (pp. 132–153). Berlin: Springer. Google Scholar
  31. Lewis, R., & Paechter, B. (2005). Application of the grouping genetic algorithm to university course timetabling. In G. R. Raidl & J. Gottlieb (Eds.), Lecture notes in computer science : Vol. 3448. Proceedings of the fifth European conference on evolutionary computation in combinatorial optimization (EvoCOP) (pp. 144–153). Berlin: Springer. Google Scholar
  32. Lim, D., Ong, Y., Jin, Y., Sendhoff, B., & Lee, B. (2007). Efficient hierarchical parallel genetic algorithms using grid computing. Future Generation Computer Systems, 23, 658–670. CrossRefGoogle Scholar
  33. Mesghouni, K., Hammadi, S., & Borne, P. (2004). Evolutionary algorithms for job-shop scheduling. International Journal of Applied Mathematics and Computer Science, 14(1), 91–103. Google Scholar
  34. Michalewicz, Z., & Fogel, D. B. (2000). How to Solve It: Modern Heuristics. Berlin: Springer. Google Scholar
  35. Mühlenbein, H., & Schlierkamp-Voosen, D. (1993). Predictive models for the breeder genetic algorithm I: Continuous parameter optimization. Evolutionary Computation, 1(1), 25–49. CrossRefGoogle Scholar
  36. Pratihar, D., Deb, K., & Ghosh, A. (1999). A genetic-fuzzy approach for mobile robot navigation amongst moving obstacles. International Journal of Approximate Reasoning, 20(2), 145–172. CrossRefGoogle Scholar
  37. Rivera, W. (2001). Scalable parallel genetic algorithms. Artificial Intelligence Review, 16(2), 153–168. CrossRefGoogle Scholar
  38. Rolls-Royce. (2002). Mearos model description version 8.31 (Rolls-Royce Internal Document). Google Scholar
  39. Shenfield, A. (2007). Grid-enabled optimisation using evolutionary algorithms. PhD thesis, University of Sheffield. Google Scholar
  40. Sims, K. (1991). Artificial evolution for computer graphics. Computer Graphics, 25(4), 319–328. CrossRefGoogle Scholar
  41. Song, W., Ong, Y. S., Ng, H. K., Keane, A., Cox, S., & Lee, B. S. (2004). A service-oriented approach for aerodynamic shape optimization across institutional boundaries. In: Proceedings of the 8th ICARCV control, automation, robotics and vision conference (pp. 2274–2279). Google Scholar
  42. Starkweather, T., Whitley, D., & Mathias, K. (1991). Optimization using distributed genetic algorithms. In H. P. Schwefel & R. Männer (Eds.), Lecture notes in computer science : Vol. 496. Parallel problem solving from nature 1 (pp. 176–185). Berlin: Springer. CrossRefGoogle Scholar
  43. Tan, K. C., Tay, A., & Cai, J. (2003). Design and implementation of a distributed evolutionary computing software. IEEE Transactions on Systems, Man and Cybernetics—Part C: Applications and Reviews, 33(3), 325–338. CrossRefGoogle Scholar
  44. Tanese, R. (1987). Parallel genetic algorithms for a hypercube. In: Proceedings of the second international conference on genetic algorithms (ICGA2) (pp. 177–183). Google Scholar
  45. Tanimura, Y., Hiroyasu, T., Miki, M., & Aoi, K. (2002). The system for evolutionary computing on the computational grid. In Proceedings of the 14th international conference on parallel and distributed computing systems (pp. 39–44). Calgary: ACTA Press. Google Scholar
  46. The GridPP Project. (2007). GridPP—UK computing for particle physics website. http://www.gridpp.ac.uk/. Accessed 23 March 2007.
  47. The White Rose University Consortium. (2007). The White Rose Grid website. http://www.wrgrid.org.uk/index.html. Accessed 23 March 2007.
  48. W3C Working Group. (2004). Web services architecture. http://www.w3c.org/TR/ws-arch. Accessed 18 October 2006.
  49. Xue, G., Song, W., Cox, S. J., & Keane, A. (2004). Numerical optimisation as grid services for engineering design. Journal of Grid Computing, 2, 223–238. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Alex Shenfield
    • 1
  • Peter J. Fleming
    • 1
  • Visakan Kadirkamanathan
    • 1
  • Jeff Allan
    • 1
  1. 1.Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK

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