Four Methods for Maintenance Scheduling

  • E. K. Burke
  • J. A. Clarke
  • A. J. Smith


We had a problem to be solved: the thermal generator maintenance scheduling problem [13]. We wanted to look at stochastic methods and this paper will present three methods and discuss the pros and cons of each. We will also present evidence that strongly suggests that for this problem, tabu search was the most effective and efficient technique.

The problem is concerned with scheduling essential maintenance over a fixed length repeated planning horizon for a number of thermal generator units while minimising the maintenance costs and providing enough capacity to meet the anticipated demand.

Traditional optimisation based techniques such as integer programming [2], dynamic programming [14, 15] and branch and bound [3] have been proposed to solve this problem. For small problems these methods give an exact optimal solution. However, as the size of the problem increases, the size of the solution space increases exponentially and hence also the running time of these algorithms.

To overcome this difficulty, modern techniques such as simulated annealing [6, 12], stochastic evolution [8], genetic algorithms [4] and tabu search [7] have been proposed as an alternative where the problem size precludes traditional techniques.

The method explored in this paper is tabu search and a comparison is made with simulated annealing (the application of simulated annealing to this problem is given in [9]), genetic algorithms and a hybrid algorithm composed with elements of tabu search and simulated annealing.


Genetic Algorithm Simulated Annealing Tabu Search Simulated Annealing Algorithm Tabu List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    K.A. De Jong. An analysis of the behaviour of a class of genetic adaptive systems. Dissertation Abstracts International, 36(10):5140B, 1975.Google Scholar
  2. [2]
    J.F. Dopazo and H.M. Merrill. Optimal generator maintenance scheduling using integer programming. IEEE Transactions on Power Apparatus and Systems, PAS-94(5):1537–1545, 1975.CrossRefGoogle Scholar
  3. [3]
    G.T. Egan, T.S. Dillon, and X. Morsztyn. An experimental methof of determination of optimal maintenance schedules in power systems using the beanch-and-bound technique. IEEE Transactionston Systems, Man and Cybernetics, SMC-6(8):538–547, 1976.CrossRefGoogle Scholar
  4. [4]
    D.E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison-Wesley, 1989.Google Scholar
  5. [5]
    J.H. Holland. Adaptation in natural and artificial systems. University of Michigan, 1975.Google Scholar
  6. [6]
    S. Kirkpatrick, C.D. Gelatt, Jr., and M.P. Vecchi. Optimisation by simulated annealing. Science, 220:671–680, 1983.MathSciNetMATHCrossRefGoogle Scholar
  7. [7]
    C. Reeves. Modern Heuristic Techniques for Combinatorial Problems. Blackwell, 1993.Google Scholar
  8. [8]
    Y.G. Saab and V.B. Rao. Combinatorial optimisation by stochastic evolution. IEEE Transactions on Computer-Aided Design, 10:525–535, 1991.CrossRefGoogle Scholar
  9. [9]
    T. Satoh and K. Nara. Maintenance scheduling by using the simulated annealing method. IEEE Transactions on Power Systems, 6:850–857, 1991.CrossRefGoogle Scholar
  10. [10]
    J.D. Schaffer, R.A. Canina, L.J. Eshelman, and R. Das. A study of control parameters affecting online performance of genetic algorithms for function optimization. In Proceedings of the Third International Conference on Genetic Algorithms, pages 51–60. Morgan Kaufmann, 1989.Google Scholar
  11. [11]
    J.D. Schaffer and L.J. Eshelman. On crossover as an evolutionary viable strategy. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 61–68. Morgan Kaufmann, 1991.Google Scholar
  12. [12]
    V. Černy. Thermodynamic approach to the travelling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45:41–52, 1985.MathSciNetMATHCrossRefGoogle Scholar
  13. [13]
    Z.A. Yamayee. Maintanance scheduling: Description, literature survey and interface with overall operations scheduling. IEEE Transactions on Power Apparatus and Systems, PAS-101(8):2770–2779, 1982.CrossRefGoogle Scholar
  14. [14]
    Z.A. Yamayee, K. Sidenblad, and M. Yoshimura. A computationally efficient optimal maintenance scheduling method. IEEE Transactions on Power Apparatus and Systems, PAS-102(2):330–338, 1983.CrossRefGoogle Scholar
  15. [15]
    R.H. Zorn and V.H. Quintana. Generator maintenance scheduling via successive approximations dynamic programming. IEEE Transactions on Power Apparatus and Systems, PAS-94(2):665–671, 1975.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • E. K. Burke
    • 2
  • J. A. Clarke
    • 1
  • A. J. Smith
    • 2
  1. 1.University of YorkUK
  2. 2.University of NottinghamUK

Personalised recommendations