Hybrid evolutionary techniques for the maintenance scheduling problem

Citation

Burke E & Smith AJ (2000) Hybrid evolutionary techniques for the maintenance scheduling problem. IEEE Transactions on Power Systems, 15 (1), pp. 122-128. https://doi.org/10.1109/59.852110

Abstract
The incorporation of local search operators into a genetic algorithm has provided very good results in certain scheduling problems. The resulting algorithm from this hybrid approach has been termed a memetic algorithm. This paper investigates the use of a memetic algorithm for the thermal generator maintenance scheduling problem. The local search operators alone have been found (in earlier work by the authors and others) to produce good quality results. The main purpose of this paper is to discover whether a memetic approach can produce better results. We describe the approach taken and highlight the variety of local search algorithms that were employed. We compare the memetic algorithms with a variety of algorithms that include the local search operators on their own and a range of algorithms that apply the local search operator to randomly generated solutions. We see that, for the problems tested, the memetic algorithms produce better quality solutions (although they do take more time about it). Of course, in practice, for a problem like this, the time taken to produce a solution is not a major issue. What is far more important is the quality of the solution. We conclude that the most effective method (of the ones tested here) is a memetic approach that employs a tabu-search operator.

Keywords
genetic algorithms; maintenance engineering; power generation scheduling; simulated annealing; thermal power stations

Journal
IEEE Transactions on Power Systems: Volume 15, Issue 1