|Author||Burke, E. K. ♦ Smith, A. J.|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|Subject Domain (in DDC)||Computer science, information & general works ♦ Computer programming, programs & data|
|Subject Keyword||Heuristics ♦ Hill climbing ♦ Maintenance scheduling ♦ Memetic algorithms ♦ Simulated annealing ♦ Tabu search|
|Abstract||The combination of local search operators, problem specific information and a genetic algorithm has provided very good results in certain scheduling problems, particularly in timetabling and maintenance scheduling problems. The resulting algorithm from this hybrid approach has been termed a Memetic Algorithm. This paper investigates the use of such an algorithm for the scheduling of transmission line maintenance for a known problem that has been addressed in the literature using a combination of a genetic algorithm and greedy optimisers.This problem is concerned with the scheduling of maintenance for an electricity transmission network where every transmission line must be maintained once within a specified time period. The objective is to avoid situations where sections of the network are disconnected, and to minimise the overloading of lines which are in service.In this paper we look at scheduling maintenance for the South Wales region of the national transmission network. We present and discuss, in some detail, a memetic algorithm that incorporates local search operators including tabu search and simulated annealing. A comparison is made both with the results from previous work, and against a selection of optimising techniques.The approach presented in this paper shows a significant improvement over previously published results on previously tackled problems. We also present results on another problem which has not been tackled in the literature but which is closer to the real world maintenance scheduling problems faced by such companies as The National Grid Company plc using the South Wales region.|
|Age Range||18 to 22 years ♦ above 22 year|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
|Journal||Journal of Experimental Algorithmics (JEA)|
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