Performance of hybridized algorithm of GA SA and TS for thermal unit maintenance scheduling

The maintenance scheduling problem belongs to combinatorial optimization problem and is traditionally solved by various mathematical optimization techniques. These methods can give the strict optimal solution for small scale problems but are not efficient for large scale problems because of the tremendous number of intermediate solutions. This paper deals with a method of solving a large scale long term thermal unit maintenance scheduling problem. The solution algorithm is mainly based on the genetic algorithms, and the simulated annealing as well as the tabu search are cooperatively used. This method introduces a reasonable combination of local search and global search. The encode/decode technique of this method represents the maintenance schedule concisely. The method takes maintenance class and extension of maintenance gap into consideration, and minimizes the weighted sum of costs and the variance of reserve powers. The performance of the algorithm is tested by applying it to the real scale problems.