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

Hyunchul Kim, Yasuhiro Hayashi, Koichi Nara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Evolutionary Computation
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages114-119
Number of pages6
Volume1
Publication statusPublished - 1995
Externally publishedYes
EventProceedings of the 1995 IEEE International Conference on Evolutionary Computation. Part 1 (of 2) - Perth, Aust
Duration: 1995 Nov 291995 Dec 1

Other

OtherProceedings of the 1995 IEEE International Conference on Evolutionary Computation. Part 1 (of 2)
CityPerth, Aust
Period95/11/2995/12/1

Fingerprint

Scheduling
Tabu search
Combinatorial optimization
Simulated annealing
Genetic algorithms
Hot Temperature
Costs

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Kim, H., Hayashi, Y., & Nara, K. (1995). Performance of hybridized algorithm of GA SA and TS for thermal unit maintenance scheduling. In Proceedings of the IEEE Conference on Evolutionary Computation (Vol. 1, pp. 114-119). Piscataway, NJ, United States: IEEE.

Performance of hybridized algorithm of GA SA and TS for thermal unit maintenance scheduling. / Kim, Hyunchul; Hayashi, Yasuhiro; Nara, Koichi.

Proceedings of the IEEE Conference on Evolutionary Computation. Vol. 1 Piscataway, NJ, United States : IEEE, 1995. p. 114-119.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, H, Hayashi, Y & Nara, K 1995, Performance of hybridized algorithm of GA SA and TS for thermal unit maintenance scheduling. in Proceedings of the IEEE Conference on Evolutionary Computation. vol. 1, IEEE, Piscataway, NJ, United States, pp. 114-119, Proceedings of the 1995 IEEE International Conference on Evolutionary Computation. Part 1 (of 2), Perth, Aust, 95/11/29.
Kim H, Hayashi Y, Nara K. Performance of hybridized algorithm of GA SA and TS for thermal unit maintenance scheduling. In Proceedings of the IEEE Conference on Evolutionary Computation. Vol. 1. Piscataway, NJ, United States: IEEE. 1995. p. 114-119
Kim, Hyunchul ; Hayashi, Yasuhiro ; Nara, Koichi. / Performance of hybridized algorithm of GA SA and TS for thermal unit maintenance scheduling. Proceedings of the IEEE Conference on Evolutionary Computation. Vol. 1 Piscataway, NJ, United States : IEEE, 1995. pp. 114-119
@inproceedings{22b6396ec977428381051722a5b07db5,
title = "Performance of hybridized algorithm of GA SA and TS for thermal unit maintenance scheduling",
abstract = "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.",
author = "Hyunchul Kim and Yasuhiro Hayashi and Koichi Nara",
year = "1995",
language = "English",
volume = "1",
pages = "114--119",
booktitle = "Proceedings of the IEEE Conference on Evolutionary Computation",
publisher = "IEEE",

}

TY - GEN

T1 - Performance of hybridized algorithm of GA SA and TS for thermal unit maintenance scheduling

AU - Kim, Hyunchul

AU - Hayashi, Yasuhiro

AU - Nara, Koichi

PY - 1995

Y1 - 1995

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0029546367&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029546367&partnerID=8YFLogxK

M3 - Conference contribution

VL - 1

SP - 114

EP - 119

BT - Proceedings of the IEEE Conference on Evolutionary Computation

PB - IEEE

CY - Piscataway, NJ, United States

ER -