Four Methods for Maintenance Scheduling

Abstract

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.