A method for maintenance scheduling using GA combined with SA

Abstract This paper presents a new Genetic Algorithms for the large scale and long term scheduling problems. Proposed method shows new genetic operation that finds the local optimum faster than the simple Genetic Algorithm with low possibility of premature and with efficient encoding/decoding technique. The acceptance probability of simulated annealing method is included in the algorithm as a criterion for the survival of individuals during evolution process. The target of this study is to reduce the computing time of simulated annealing based method and make the solution be more accurate than that of the simple Genetic Algorithm. In the proposed method, this time, a part of the proposed method is implemented to real scale thermal unit maintenance scheduling which covered several consecutive years.